Learning contour identification system using portable contour metrics derived from contour mappings

ABSTRACT

A system and method that transforms data formats into contour metrics and further transforms each contour of that mapping into contours pattern metric sets so that each metric created has a representation of one level of contour presentation, at each iteration of the learning contour identification system defined herein. This transformation of data instance to contour metrics permits a user to take relevant data of a data set, as determined by a learning contour identification system, to machines of other types and function, for the purpose of further analysis of the patterns found and labeled by said system. The invention performs with data format representations, not limited to, signals, images, or waveform embodiments so as to identify, track, or detect patterns of, amplitudes, frequencies, phases, and density functions, within the data case and then by way of using combinations of statistical, feedback adaptive, classification, training algorithm metrics stored in hardware, identifies patterns in past data cases that repeat in future, or present data cases, so that high-percentage labeling and identification is a achieved.

The present application is related to and claims priority to U.S.provisional patent application, “POINT-TO-POINT REPRESENTATIONS OFENCLOSURES OR LINES REPRESENTING OBJECTS OF GROUPS OF OBJECTS WITH DATAFORMATS,” Ser. No. 62/081,513, filed on Nov. 18, 2014;

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention is in the technical field of contour detection andidentification within file format data types for use in patternrecognition analysis.

Description of the Related Art

Image detection algorithms locating objects (people, mechanical, signalwaveform representations, or objects of any physical nature), withindata formats, identify the objects for a possible purpose of trackingthe object within the image. They do so whether objects are stationary(image that does not change in time or frequency) or dynamic (change intime or frequency); that is, they find an image object, but do not takethe objects found outside of the source for further processing. As aresult, current technologies, in image processing analysis work, desiresthe computational process to remain with the original source whenperforming calculations for any attempts to make object identificationpossible.

Current technologies chose not to remove the image objects searched forfrom the data set so that objects found may be portable to otherapplications that may wish to further analyze the image data instances.Current technology makes no attempt to transform the image it finds intoanother numerical quantity source that is not object related in atypical sense; that is, current technology does not talk about an objectas an equation, it speaks of an identification processed by showing theprocessed image within a modified version of its own data format source.Current technology stays within the realm of the image and talks aboutany identifications that need to be stated to a user of such a system asa reference to the original data set and its data values from whereidentifications came.

There are no current methods that use metrics of recognizable andunrecognizable patterns, of as small as 1 pixel, to identify an object,instead. There are no systems that use such methods to group suchpatterns so that a pattern of one dimension can be paired to an entirelydifferent dimensioned pattern in an effort to identify an object instill another dimension. There are no current methods that use a patternas a collection of metrics to identify an object, whether or not thefinal identified object is identifiable by human visual experiences orexpectations, and make that representation portable to other entirelydifferent computer system designs. There are no current methods that usea pattern grouping method, whose output from a system of hardware isportable and independent of the source to define an object andfingerprint it without having to reuse irrelevant data of the source. Nocurrent method uses contours, created from contour maps of data sets(typically associated to the study of topography) to create contourmetrics for a new type of system now introduced as a learning contouridentification system.

These novel metrics herein are called contour metrics and are derivedfrom contours of contour mappings, where each contour of the map has itsown set of metrics stored as container sets usable by the design of thelearning contour identification system. The metrics of the containersets are typically statistical density sets, areas sets, coordinatepoint sets, and other metrics created and determined by a system ofhardware components that make up a learning object identificationsystem. Other container sets are subsets of the same, or other analysissets that could very well be the output of a mathematical processes,machine code instruction sets, or subsets of its own container set. Thecontainers group together to define the objects or the groups ofobjects, and to essentially leave out irrelevant information of the datasource for the benefit of pattern localization and final labeling. Alldecisions and conversions and storage locations in memory is determinedby the learning contour identification system by creating a new, if youwill, mathematical representation of the patterns. Essentially, thecontainers, then, by supplying the metrics as memory location elements,or variables (metrics of the individual containers) make the learningidentification system a function processor as the metrics are plug-inmodules to a learning contour identification system to make it perform aprecise way that it also determines autonomously. Basically, the systemand its metrics create its own encryption code set to describe a datacase that has micro patterns that are found to re-occur in sets of datacases having similar data pattern representations only recognizable bythe learning system hardware that created it.

The current technology does little for the purpose of furthermathematical or statistical analysis on what can be learned from theobject after an object is found like on a line. Current technology mayidentify a line, but does not provide a searchable set of metrics onthat line that has relevance to the image it came from. Currenttechnology, therefore, cannot allow the user to walk away from thesource image with some pattern, in hand, as a completely differenttranslation but having the same identification and same meaning to theapplication using the information detected. Current technology prefersthe user and the applications using the data to remain close withsource, and requires the system to show the user the object found withinthe source file, or to use the source file as a reference. Currenttechnology does not attempt to “transform” an object into anotherquantity so that it can leave its data format environment and still havean object identity. Current technology does not attempt to provide auser with a process form derived entirely from hardware and itsapplication software control, which not only identifies the shape, butfingerprints the pattern by a sequence of metric representations of apattern.

SUMMARY OF THE INVENTION

The present invention is a systems of transforming data into contourmaps and its individual contours into contour metrics (a whole thatunites or consists of many diverse elements that can be identified as aline or closed shaped, one or multidimensional) for the purpose of usinga plurality of contour pattern metrics of past data to identify contourpatterns within present data using the learning contour identificationsystem to transform inputs, and to then manage and create these patternsand contour metrics in both training (past data) and test (present)cases.

As a system and method, a complete summary further includes:

A method performed by one or more computers, capable of operating inplural parallel, plural serial, or singular format processor systems. Amethod comprising a means of obtaining electronic or mechanicallygenerated data from a single or plurality of electronic or mechanicalmeasurement devices, or image capturing devices,

which configure said data into mechanical, machine, or electroniccomputer readable data representations of a data case,

allowing for pattern, contours, and background wholes, with data havingwholes with or without boundaries, or fillers, that are numeric, binary,machine code, or symbolic, or computer hardware readable types, in partsor in combinations of parts, to represent enclosure representations ofdesire to user or machine,

whose enclosures may be identified by pattern, system, or contour shape,by self-learning algorithms, mechanical mechanisms, or human generatedreal-time pattern generators, in singular or plural dimensional form,with distinguished or undistinguished human shape such as signals, ofsingular or plural dimension, having or not having physical unit valuelabel restrictions of said whole, part, or combinations of whole andparts being singular, or of the plurality,

with all wholes having system identifiable wholes representing systemfeedback needs in singular or plural form, by same singular or pluralsystems of machines for mechanical processing methods, computerprocessor methods, or human interface processes,

which manually adjust data of computer systems, and/or by reinsertinginto said computers, or into a machine/computer system's communicationprocess, for purpose of enhancements of outputs, security of inputs, orreduction of inputs of said system,

finalizing data output by storing said data measured into a machine ofmeasurement equipment's, or system's attachments, without restriction ofdimensional, serial, matrix, or mathematical file type format,compressed or uncompressed,

or finalizing data output in computer file type format stored in amobile, online, or transferable format that can be read by a computer,human, or mechanical retrievable format usable by said patent

which can be then readable at present or future time lines without needof identical preprocessing of said machine, computer, mechanical orhuman inputs, and are readable by said mechanical or computer processingmachines or human input systems designed for the Processing of said dataobtained, wherein processing transforms to a another mobile use of data,or data storage, disconnected from original data storage type, datasource, and measurement purpose, all patterns, images, desired orunidentifiable to human visual expectations, into another file formatcollected and trained on, in part, or in whole, or in planer formatwhich may be a layer of a two dimensional projection within amultidimensional axis format of data, to be collected and stored as amanifold representation processing code,

where a manifold is a single contour metric, of a contour mapping of adata case, which is a coordinate point set enclosure of an pattern thathas thickness, or of a line made to have thickness by available, orintervention insertion, of neighboring boarder points, of numeric orsymbolic format, of pattern defining a known or unknown shape enclosure,to a metric level of determination set by computer, mechanical,biological entity, or human interaction,

and where a manifold representation is a manifold storage of a singularor plural detected contour pattern metric set, stored as arepresentation coded data set of contours of patterns, described by eachmanifold grouping, decided upon by feedback actions of computeralgorithms, computer or mechanical firmware, electronic componenthardware or firmware, software program, or mechanical or humanintervention, that are singular or plural in whole,

with manifold representation code elements, of singular or pluraldimension, in storage format of singular meaning of representation ofdetected pattern whole, singularly or in plurality in combination withother manifold representation codes of single or plural dimension,

with each manifold representation code element, of possible singular orplural manifold representations, of single or plural elements ofmanifolds, of new starting level or beginning, grouped or ungrouped,higher dimension of layer or grouping, without precedence, as decided onby computer, human intervention, mechanical device, or computer process,

where each manifold representation code, of single or plural quantity,in single or higher dimension, identifies singular or plural grouping bycomputer, human or biological intervention, mechanical, or electronichardware, firmware, or software, which is of a single or plurality ofmeasured data acquisitions, retrieved by a data acquisition instrument,in part, or in whole,

where the manifold representation code whole is of singular, or pluralform, with individual manifold representation codes of plural elements,or multi-dimensional elements,

for the purpose of identifying a inventions hardware processor, learned,mechanical or computer or electronic component, or electronic displaygenerated, pattern of interest, within a data acquisition data set, orwithin its own manifold representation code,

of a computer algorithm without human controlled input and outputparameters of processor limits, or with same human controlledparameters,

with parameters determined by manual, mechanical, or computer firmwaretraining, of mechanical or computer processors, and their algorithms, orfrom feedback error optimizations within said systems of hardware, frompast data in plurality, or present data in singularity or plurality, astransformed by same system, or via patent herein, in iteration, orfeedback format, following hardware and human interventions measured, orcomputer algorithm measured, and patent transformation iterations,without precedence,

with input source data to patent processor hardware, in plurality, orsingularly, requiring measurement data acquisition, occurring at leastonce following in singular or plural acquisitions and storages, in partor in whole, in future or present timelines, or in real time processingthrough human or biological intervention, or computer or mechanical orelectronic display, or electronic component intervention,

for use with, in part or in whole, newly acquired data sets of start, orfuture acquisitions, or future and present, or real-time, patentprocessed, hardware, data acquisition patent transformationacquisitions, to be patent hardware processor characterized, for finaloutput pattern identification by patent hardware characterizer

by means of choices made by computer algorithm, electronic hardware,display output stored and retrieved, in plurality or singularityrepetition,

or by firmware, containing, or interacting, with patent, or human orbiological being (disease, genome, cat, dog, chimpanzee, etc.), asdesired or by computer processor, electronic hardware component,

by findings of generated output report, of unidentifiable patterns,within acquisitioned data, input to patent processor, unlabeled,

or human or biological recognizable feedback, labeled

or by manifold, or manifold representation codes, used in present,claiming future or present pattern identification occurrences, in termsof probability, a statistic, a mathematical representation of variables,a signal, or a metric, all or combinations of, defining degree ofsuccess of detection correctness in label predetermined, orapproximated, through learning algorithms, written in software orfirmware, or implemented by singular or plurality of electroniccomponents,

with singular or plurality of successful detection, hardware systemprocessed manifold representation codes, media or medium stored,

that is retrievable, for use in future or present processing, bycomputer, by human, mechanical system, or by electronic component ordisplay device,

defining past or present or future patent pattern identificationclassification output finalization accuracy, of plural or singularmeasurement data acquisition input transformations, or oftransformations that have been preprocessed in a plurality form, as alearning event,

which is processed by algorithms whose input is the output by systems ofelectronic, human intervention, electronic display, or mechanical, orcomputer electronic components of singular or plurality combinations,

or is processed by manifold representation codes created by said patent,for future accuracy decision metrics, or present or future or pastaccuracy decision metrics, or for real-time accuracy of detectiondecision metrics,

for use as patent characterization output of pattern identificationprocessor hardware system, with output for display, or softwareprocessor, for reporting or analysis determination of detectioncorrectness,

where correctness metric representations are probability or statisticalmetrics, usable by human, by computer, or by mechanical hardware, withina defined hardware system of electronic components of singular or pluralcombination, or within a hardware system with firmware, or controlled bysoftware algorithm codes, or electronic singular or plural combinations,

or a human determined measurement correctness margin metric, with humanintervention passed to patent transformation of data acquisition of thisclaim or capture device,

with patent result output, characterization, metrically describing adegree of accurate detection, of manifold representation codeapproximations, of detected output pattern label of processcharacterizer, of some margin of error,

with repeatability and re-occurring act of nature described throughmultiple findings of micro-level reoccurrences, found in patenttransformed data to manifold representation of pattern, of learnedinterest, determined from patent transformation and characterization,without need of future data, tested or untested, and without need orre-measurement and re-process of patent transformations,

in representation of a look-up table human or computer hardware formatretrievable where training sufficiently predicts future to statedprobability of identification determined by training, representative ofconfusion matrix format or its plurality,

which is a human, computer, or mechanically labeled, singular or pluralsystem, of singular pattern of interest,

or a biological labeled pattern of interest, of same system of hardwarepossibilities, human or not human labeled interest, signal labeledinterest of single or plural dimension, human or not security labeledsignal interest of single or plural dimension, human or not, networkcommunication used labeled or unused label of detection, analog orcomputer received data types, of known or unknown information sourcesthat have no identifiable human interest, labels, called a manifoldrepresentation output of the characterizer,

where output manifold representation code wholes, singular, or plural,represent a singular or plural form of output identified patternmanifold representation code, labeling detected output of pattern, orfor further analysis, decided by training of interest, or not, by human,computer, or electronic component of singular or plural combinations,

with output manifold representation code, of past or present, orreal-time processing, interest decided upon by mechanical device,computer program, computer firmware, hardware firmware, or biologicalinput,

where data is numerical, binary, symbolic, singular, or of pluralpattern groupings, decided by singular or plural computer processing,using human manual interface decisions to data grouping, or computerfeedback evaluation and re-input of analyzed data grouping,

of data captured pattern manifold representation codes, of patterndetected groups of singular or plurality form, of human identifiablepattern wholes, or of computer, hardware, machine, displays, orelectronic component singular or plural combinations of identifiablepattern wholes,

that provide final manifold representations, as a stop process of patenthardware, reporting output of a characterized detected pattern,determined from single, or plurality of repetitions of trainingprocessing, and characterizations processing, of training manifoldrepresentation codes, or of testing manifold representation codes, orcombinations of the two, making one iteration of singular or pluralityof input training data acquisitions,

generating defined plural or singularity of manifolds, manifoldrepresentation codes of plural or singular groupings, singular or pluralstatistics, or number or symbolic representation metrics, for finalclassification output, that are not manifolds or manifoldrepresentations codes, but metrics providing same metric, of numericalor symbolic metrics, defining patent pattern detection process accuracy,

which provide a metric of accuracy of same pattern detection, resultingfrom training decision rules, created at end of singular or pluralrepetition of patent hardware system process, of training algorithmoutputs that are not rules, but learning to report optimum imageidentification labeling, or unlabeled, of singular or plural form,

Where the term “manifold” is synonymous with a single contour of acontour mapping as a container of metrics describing the contour andwhere “codes” and “code” is synonymous with the sequence of metricsstored in the manifold, or single contour container of a plurality ofother manifolds, codes, metrics, and contour mappings, unless statedotherwise in the specification or claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a step-by-step flow diagram of the preferred embodiment of aprocess of grouping contour pattern metric sets of the set of contoursof a contour mapped training and test case.

FIG. 2 is an example of a matrix of a format data type. There are manydata formats and all can be used with this invention as there are simplemethods to transform one data format type into another. Images andgraphics are defined in digital data file formats. Currently, there aremore than 44, but there are said to be 44 that are frequently used. Itis better to generalize and state them as being grouped by type, so thisinvention covers: raster formats, pixel and Web file formats,meta/vector file formats, bitmap file formats, compression file formats,radiometric file formats (temperature and image), GIF image fileformats, animation file formats, transparency file formats, interlacedand non-interlaced file formats, JPEG file formats, and progressive JPEGfile formats. (This invention (FIG. 1) is not limited to only the knownformats as any unknown file format can be converted to one that theprocess of the invention can use.) All “types” have a magnituderepresentation of the image and patterns it contains, and so for thepurpose of this patent application, all “types” can be used. The datatype in the data format may be in numerical or bit representation ofshades of colors or shades of gray. Or, they can be translations of 1'sand 0's (or 1's and 0's themselves) into magnitudes. In FIG. 2, themagnitude 1 represents a background magnitude value of one, where anyother numbered magnitude could represent an actual pattern in an image.In this case, the example is saying that a magnitude of 5 represents apattern, not necessarily the same as the other pattern of magnitude 5,within one image. The figure, as given, represents a separated imagepattern, from other patterns, by one unit. Patterns without a one unitseparation, on its all sides, will be considered a grouped pattern. Theentire matrix represents the entire pixel-by-pixel representation of thedata format image storage container it is used to represent. In theexample, then, it is a 5×8 image of data points. (Note: image andpattern are used interchangeably. An image can be an pattern, forexample. Or, an image can be an pattern within a image.)

FIG. 3 is a generated display of the contour pattern metric set definedenclosure (I interchangeably refer to the contour metric set as a“manifold” as a manifold is a container, and do so to simplify writing)of three patterns: 19, 20, and 21. The combining process or contourgrouping process, FIG. 1, determines these manifold patterns. In theinvention, FIG. 3's results represent choice 12, and 14, of FIG. 1; thatis, single manifolds are to be found.

FIG. 4 is a generated display of the contour metric set definedenclosure (manifold) of three patterns. The invention determines themanifold patterns according to choices 13 and 15 in FIG. 1. For choice13 and 15, “two” manifold patterns were chosen to identify the patternit encloses. The locations of the divisions of the manifold patterns aredetermined by the amount of spacing between patterns, as shown in 22.They are equally spaced, but are not necessarily required to be equallyspaced in all applications of FIG. 1.

FIG. 5 is a generated display of the contour metric set definedenclosure (manifold) of three patterns. The divisions of the manifoldpatterns are determined by the spacing between patterns as shown in 22,FIG. 4. The invention determines the manifold patterns according tochoices 13 and 15 in FIG. 1. They are equally spaced, but are notnecessarily required to be equally spaced in all applications of FIG. 1.In FIG. 5, twenty divisions are shown so as to maximize “correct arearepresentations,” of the shape manifolds, created by the intensityvalues of 23, 24, and 25, through process, FIG. 1.

FIG. 6 is a generated display of the contour metric set definedenclosure (manifold) of two patterns. This figure uses another matrix toshow that choice 13 and 14 are used in invention FIG. 1. The range ofpattern values grouped is 4.5 to 5.22 which is determined by the numberof contours desired and set within the learning contour identificationsystem hardware. There are three possible pattern classifications asdefined by intensities 26, 27, and 28, but there are two patternmanifolds selected by FIG. 1.

FIG. 7 is a generated display of the contour metric set definedenclosure (manifold) of a pattern in a multi-pattern image. It isembodiment application example three of the invention shown in FIG. 1.This 33 is a 1/32″ thick 18K gold neckless fallen randomly to thecarpet. This 34 represents a radiometric type digital image file formatof the thermal image 34 of the neckless in 33. This graph 35 representsmany manifolds of points generated by 12, and 14, and by 12, and 15, andby 13 and 14 of invention in FIG. 1. This graph, 36, of manifold points,represents 13 and 14 of invention FIG. 1, re-generated from mathematicalrepresentations created by manifolds of points of 35, processed throughFIG. 1. The preferred method of four techniques of 10-17, of FIG. 1, 18,is used in combination in FIG. 7.

FIG. 8 is a top level description of a Learning Contour IdentificationSystem (LCIS). These are the hardware components that make up a generalsingle LCIS system. Item 36 has a controller 34 that processes theinstruction set micro code of the learning system through the datapath37 and 38. The contour pattern metrics are stored in the 39 via thedatapath 35 and are created within 36 by way of 34.

FIG. 9 is a top level description of the Learning Contour Identificationsystem process showing a high level operation of FIG. 8. This figureintroduces the system as a training processor communicating with theclassifier processor. The training processor gets training case data 40,of one or more data cases, learns from the past training data 41 through44, and sends the output 45 generated by the training processor to thepattern identification processor 48 where 47 retrieves test cases anddetermines from 45, by way of 49 through 53, iterations of 41 and 48,until the LCIS displays the output 54 and stops. Options to increasecontours in training, training and classification, classification only,are determined by user and by confusion matrix outputs processed by theLCIS system.

FIG. 10 is a low-level description of the training processor, 55 and 64,and instruction set micro code, 56 through 61. Training case data iscaptured 56, transformed into contour metrics, 57 through 60 and 62, setto training, 63, trained on in 65 through 72, where the training moduleprepares the output to be sent to the contour pattern identifier, 72 and62.

FIG. 11 is a low-level description of the test case contour patternmetric set classifier processor, 73, and instruction set micro code, 75through 82 and 83 through 88. Test case data is captured, 75,transformed into contour metrics, 75 through 78, training black box ortraining rule-set code pulled in 79 and applied to contour metric in 80achieving contour pattern identification in 81 and compared to trainingsconfusion matrix in 82. If the statistics found in 82 are too low in 83,84 and 85, then it returns to training to increase the contours andre-run FIG. 10. But if the threshold is still met, then only increasethe contours in the classifier 87, and repeat classifier. Once theclassification is found to be as optimized to past data statistics asdefined by the confusion matrix found by the training process, theoutput is sent to memory 88 and displayed.

FIG. 12 is the low-level flow diagram of the contour pattern metricsprocessor's instruction micro cod set 89 through 98. Here it can be seenthat the preferred embodiment of the basic contour metric, or manifoldas a container of all contour metric sets. A contour pattern metric set,or manifold for short, for the preferred embodiment contains at least alabel 93, a coordinate point sets 94, and statistic metric 95. The LCISdetermines if other metrics are desired by the training module in 96through 98.

FIG. 13 is the high-level description of the system describing acomplete learning contour identification system composing of the userapplication control 101, the training module, and the classifier, andthe results when metrics are used in both. A LCIS system can be systemsof system as shown in 99 through 110. The LCIS system of 104 through 106can be a grouper of the contour pattern metrics, learner of the contourpattern metrics grouped and iterated through with the grouper 104, andboth 104 and 105 can work with memory 105, or interact with plug-inmodules 102 through n instances of 103. Then the system output of theblack-box learner or the rule-based learner 107, can be sent to theclassifier 108, whose output is stored in 109, and then displayed in 110and the whole process repeated. This whole system 100 to 110 can beanother LCIS system that can be a plug-in module 102, as well. Tocontrol the whole process which turns it one, and customizes,application software can be developed as a module 101. This is necessaryor there is no way to turn it on and operate it.

FIG. 14 is the memory hardware describing how the contour pattern metricset instruction sets are stored in memory by learning contouridentification system. A single contour pattern metric set, from acontour mapping, is stored between two addresses 112 and 118. It isappended too based on the LCIS needs 120. A basic preferred embodimentstructure of a contour pattern metric that can be stored as an externalmemory container for portability, consists of a coordinate point set,113, a filler, 114, a statistic where Gaussian Misture Model outputcomponents are stored 115, some more math outputs like possibly areaoutputs of row, and columns, 116, and metrics of other contour patternmetrics that have been group through contour maps of possibly otherdimensions. Item 117 then is a set of metrics 113 through itself 117, orbasically iterations of contents between 112 and 118 appended between112 and 118. Items 119 through 121 represent a repetitive process ofadding more contour patterns to the metrics between 112 and 118. Theresult is a code, a contour pattern metric code, or a manifoldrepresentation code. It defines only one pattern and can be used to drawthat pattern, and all metrics can be used to by other programs tomanipulate these metrics. This means that the finger print is coded intoa sequence of outputs stored as sets, and since they are all derivedfrom the contour, the pattern, can be used in a training environment, orby itself without training at all. Training allows the user to use themetrics of past data to determine of the pattern exist in future data.If the statistics are Gaussian, then because of the Central LimitTheorem, those patterns described by Gaussian patterns will repeat inthe future so the confusion matrix of the past data will be highlyrepresentative of the future. This is very important to the preferredembodiment as it means that voice identification is easily identified inpresent data if past data is given as voice is natural, and thereforeits micro patterns found within a signal capture will definitely repeatin the future. It also means that the voice can be removed from noise.

FIG. 15 is another low level description of the contour mapping microcode instruction set of the LCIS. The figure is a simple exampledemonstration of how a contour may be developed. The example starts withmaking up a 4 by 4 matrix of pixel intensities. It finds minimum andmaximums in 123 and 124 and reads the number of contours the systemwants, and divides the shortest distance between these areas in equalintervals of 5, for example in 126 and 127. Then the contours areconnecting point between the divisions like 25 to 25, or 55 to 55. Amore detailed example can be grouping the ranges of intensities as wasseen in FIG. 6 where the range was between 4 and 6, essentially. Item136 shows how a two contour pattern metric point set is created in 139.The contents in 139 is that metric information stored as 113 in FIG. 14.

FIG. 16 is another low level description of the contour mapping microcode instruction set of the LCIS. This figure represents an example ofthe statistic metric, which is really just an example of 116 in FIG. 14shown as a preferred embodiment of a contour pattern metric when thetraining is a Classification and Regression Tree rule-based trainingmicro-code set. The contour pattern metric set of coordinate point setsare described by 149 and 150. The fillers of 1's are place in thecontours of each. These can be weighted unity fillers. Then, the sumsalong the x and y axis give the histogram bins of 152 and 157, havinghistogram envelops 156 and 146. The Gaussian Mixture Model componentsthen are possibly 154, 153, and 155 as well as 147 and 148, and 151. Themean and variances represent the location and variance of each of thesecomponents. As Gaussian Mixture Model components can be added orsubtracted, learning may add 154 to an entirely different set ofcomponents from an entirely different contour metric to identify moreprecisely the contour pattern to be classified. This means that the LCISsystem finds micro patterns and records them for future use and which noother state of the art program can do. And, the result is portablemeaning at any time the pattern can be recreated from the metric withoutever needing the photo again.

FIG. 17 is a generated display of the contour metric set definedenclosure (manifold) of a pattern in a two pattern image. It is anotherembodiment application example of actual implementation of theinvention. Image 161 through 163 represents a cancer cell given as asample image provided as test images by Mathwork's MatLab softwareanalysis test image directory. That image in its entirety is retrievedfrom a file format of TIF converted and transformed into a contour mapwhose contours are transformed into many contour pattern metrics by thelearning contour identification system. The boarder of the one patternin 162 would be the border or borders of elements inside as 163 of thedark circle to identify these two cancer cells (the other 161) aslabeled objects when using current state of the art attempt to do tolabel the object as a cancer cell which is what it is. Image 165 and 166represents the output of FIG. 1. The contour learning identificationsystem described herein provided 165 and 166 at the conclusion of itsprocess. These two images are the only images the learning system needsnow and learning is on the metrics only, no longer is learning on thedata within the image capture. The learning contour identificationsystem using the contour manifolds found two patterns 165 and 166 andput them together to classify the object above. All backgroundinformation of 161 and 162 is now considered irrelevant to what thelearning contour identification considers necessary to describe thecancer and that is determined autonomously or by user intervention ifthe user desires to use the application plug-in module to modify it.And, as the contour pattern metrics describe 165 and 166, the fileformat the metrics stored in by the learning contour identificationsystem can be taken to any application software to reproduce the imagefrom its contour pattern coordinate point-set metric, losing noinformation of where it was in the image as location is stored in theother metrics, and through other metrics taken, the object can besqueezed or morphed without changing identity.

FIG. 18 is a generated display of the contour metric set definedenclosure (manifold) of a pattern in a two pattern image. It isembodiment application example two of the invention shown in FIG. 1.This image is a communications signal, in the time domain, taken from ahardware data set, stored in a digital image file format type, JPEG. Thebackground results of noise 171, also seen in FIG. 18, are generated byprocess choices 12 and 14, of FIG. 1; that is, they are single manifoldenclosures. The identifier 167, in the figure, represents the choice ofmultiple manifold patterns in process 13 and 15 of FIG. 1, which areused to track and identify patterns pointed to by arrows. It representsthe grouping by way of FIG. 1 in the learning contour identificationsystem as it detects peaks in amplitude, and detects location in time ofthese peaks, in 168 along the x-axis. All data in 167, and thosemanifolds surrounded in the background of 171, have a contour patternmetric set description that can be removed from the image without losingpattern identity (identity is given by the manifold representation of18, FIG. 1). As in the previous embodiment example of the cancer cell,the entire signal is now in a metric, which means these metrics canencode the signal and then remove from the image set the pertinentinformation the metrics to be brought to another station to decrypt thesignal. This metric then, is impossible to decrypt by any hacking orreception of the signal transmission as the metric describes theidentification, not the image. You are left with communications thatcannot be decrypted by interception by any means as the learningidentification system created the metrics that define what it saw. Thepreferred method of four techniques of 10-17, of FIG. 1, 18, is used incombination of FIG. 1, in FIG. 18.

FIG. 19 is the processor instruction set showing an iterative process ofhow the statistics in the preferred embodiment would be used to completethe contour metric of one contour of a contour mapping of a data case.This one contour is referred to as finding the manifold, again, findingthe contour pattern metric set, which is just a top level description ofa contour mapping container of metrics. Item 162 is a blow up of 170 inFIG. 18. It represents the LCIS locking onto a pattern of interestcreated by the training FIG. 10 working with the grouping of FIG. 1 in aiteration process of locking onto the object by increasing contourswithin the metric container set. You are seeing five contour metrics 117of FIG. 14 that will be used to locate the peak amplitude found betweenlocation 154 and 160 on the x-axis. This means Fourier Transforms needto be performed in similar uses, as the locations can be used as timeelements as long as the image capture has a known scale. For example,that example would mean the image plug-in module would be an instrumentattached to a oscilloscope which has time gradients that are known.Those gradients would be transferred to the metric as the metric isportable. This means that all that is necessary is to have the contourpattern metric stored in external memory, such as a USB drive, andsimply plotting out the contours and analyzing or using a LCIS module asdescribed in 13 do that for you autonomously. Again, there is no usageof the past file the pattern came from. The pertinent information hasbeen retrieved, locked onto, precision increased (four contours wherewhat optimized this before exiting), and displayed and recorded. As thepeak is only of interest, all the data left can be noise, which, ofcourse is another metric that can be used to link as well. For example,a speaker may always be in one sort of environment. If that environmentis contained in the signal, it can be linked as well, but if it is notrepeatable to a confusion matrix of performance values, the autonomouslysetup LCIS will not pattern it unless the user, through 101, of FIG. 13,decides to set auto mode to manual mode settings to operate LCIS in acontrolled means that stop micro-code in steps or in process sections.

DETAILED DESCRIPTION OF THE INVENTION

Terms are defined so that they do not become limiting parameters of theinvention, but rather a means of written communication of the methods,means and apparatus of the invention when necessary to point outparticulars.

Manifold: A contour, of plurality of contours of a contour mapping of adata case, has a plurality of metrics to it. It is a contour containerof metrics which is used interchangeably in this document with contourpattern metric sets, or contour metric containers. It is used withmanifold representation code to say that it is a top level descriptionof a single contour.

Manifold Code: Code is simply the metrics defined by the manifold, orthe contour patter metric set container. It is a code, because it is thesequence from which the processor reads from memory the description ofthe pattern it identifies and to be used in the training module and theclassifier test case.

Case: In the application of the invention the term “case” is generallyfound to be paired with data, training, or test. Data has a file format,a data format, and a data type, and therefore, so does a case. Data canbe internal, and can be external, and therefore, the case can beconsidered used in that way as well. Case, in simple use of terms,represents data of any data format (i.e., analog, digital, symbol, etc.)or mixtures of any data type (char, int, and so on), to be acquired(internally or externally) and processed (stored retrieved asappropriate as a file format or data format) in no particular order byno particular means as long as the output justifies the means. For ageneral example, a data case can be received in compressed format sentserially in a communication channel which could be a real-time receiveddata case. It is stored in a format readable by a system using it. A setof simple examples would comprise: data formats which can be compressedsuch as MPEG or jpeg, or an image formats such as jpeg, png, eps, gif orany format or data type recalled to form an image, or a movie, or audio,or combinations of file formats, data formats and data types. Or, it canbe a web based format such as HTML, or even a non-numerical data type orformat such as symbols as these can be converted to any other desiredformat or data type by a process method designed to do so.

A Training Case: A case of data that happened in the past. It has aknown label. For example, one is to take a 100 pictures of chairs. Thereare only two types of chairs in the pictures: Rocking Chair, andNon-rocking chairs. The label's examples would be, possibly RC, andNonRC or similarities, for each training case as given by the systemusing it, or given by the system capturing it. The point is that it isof past data to be used for training a learning module.

A Test Case: A case of data that happens now, or achieved in thepresent. It has a label, but it is of uncertain labelling. The labelingexamples would be possibly blank, NA, or a guess, or user supplied. Theformat can be converted to a format necessary for the learning to thetraining case that will be necessary for deciding, for example, if thetest case was a Rocking Chair, or a Non-Rocking chair as seen in pastdata which were training cases.

Communication channel: The path that data is received or transmittedover. Simple examples, not taken as a completion of the possibilities,can be via a waveguide device such as a computer bus, which may consistsof wires which are also wave guides, or over the air by way of atransmitting via an antenna and receiving via antenna where the channelnow becomes the air space between transmitting device and receivingdevice. The primary point is that data is sent in a format that isnecessary to be received by the receiving device and it is done throughthe means of a channel of communication between a user, machine, orcombinations of same.

Recording or Data Capture Device: The device used to take informationand store it into a usable recordable format that is stored in volatileor non-volatile memory for immediate processing use, or later mobileuse, or combinations of same. Some examples to be considered may be acamera, a scanner, a voice recorder, a microphone, an eye scanner, athermal imager, CAT scanner, a scanned printout of a paper graph, or theoutput of an application package (printout to paper then scanned, orimage then saved, for an example) that plots an equation's dependent andindependent variables. It can also be a real-time operating system(RTOS) that serves as an application processing of data as it comes intoa system.

Data: Are possibly point instances within a container such as a file, orlocated between two address locations in memory. The data within thecontainer can be numeric, can be wrapped into a file of some data typestructure commonly referred to as a digital file type structures, orcould be an output of a capture device that is of a RTOS (real-timeoperating system) nature, for example. It can also be a waveform, or canbe multi-dimensionally defined by some multi-dimensional coordinatesystem commonly known to graphing. It can be vector based and describedby a vector space it is defined over, or that it explains. These arejust a few examples of “data”, and their formats or data types can be:numerical, binary, hex values, or a simply in a format readable by somesystem that desires to use it. The point is, data is never a limitationto a system as it is handled by the transformation processor of theLCIS.

A Contour: In this invention, an enclosure of data instances having nospecific shape. For example, a contour can look like a chair, but maynot be labeled as one without having other contours combine to make thatclaim. This is a primary feature of the invention in that contours incombination are a means to an identification of “data” or its parts, notnecessarily the shape they make of the object that is user identifiable.What is not clear is that the contour does not necessarily take on theshape of a chair, and a shape of a spot for example, but a contour couldtake on parts of the chair and parts of the spot to form a contour thatwould only be identifiable to the system, and not necessarily the userunless a high degree of visual attention is paid to each micro part ofeach item making up the contour. What is happening is that unnecessaryinformation of doing a job, that is to label the chair, is removed fromprocessing all the data in the case, thus giving a hardware system agreat deal of data reduction capability reducing computationalcomplexity, and when considering multiple “cases”, high-dimensionalspaces can be converted to fewer dimensional spaces.

Metrics: Metrics are precisely defined as given herein. The term“metric” is a means of discussing the representation of a group ofquantifiable measures, having labels naming the group of measures. Foran example, to ease the extreme complexity of the term, take statisticsas a metric label. Statistics, in one capacity, can be described bysaying a collection of means and variances. A “metric”, as used by thepatent, can then be the mean, or the plurality of means as a vector ofmeasures. For example, say we have 10 numerical representations of meansof test scores, each mean representing a use of one year worth of afinal exam scores. Metrics, in a system described by this patent, haveto be stored in memory in a manner that accommodates order anddimensional size as they are processed. This is to imply that begin andend memory address locations are dynamic in that they expand andcontract. It also implies that a metric and its contents have a locationwithin memory that is system trackable, which further implies that orderas processed and order as stored in memory be system determined to allowfor metric parts or wholes to be extracted from memory correctly. Forexample, what if the metric of means, in the example of test scores,includes two additional test score years?Memory of the metric locationlabel, “Statistics”, with sub-name, possibly “means”, would be anaddress change of “means” from holding 10 items, to now holding 12 itemsof numerical representations of mean calculations. Metrics are onlylimited by the memory storage process of the system (i.e., a systemcontroller capable of storage maintenance from 10 items to 12, andcapable of going from “means” to “means and variances”, for example),and its acquiring process (i.e., execution of machine language mathinstructions, for example), neither of which have to be complex instorage tracking, they just need references that can be tracked for usein a system which uses them and stores them and accesses them in amanner that is an efficient manner so as to accommodate iterations ofaccess. Memory also can be volatile or non-volatile to handle largedatasets. The method given is only one possibility. Further, “metrics”can be thought of as math calculation “results” that are stored asvectors, or sets, or labels, and so on. Or, can now be sub-sets evenwithin their own metric, to become the metrics elements, calledsub-metrics. The main point is that a metric is a sequence of datavalues (each data value being the 10 values described as mean values,for an example) that have a specific meaning in why, and where, it ispulled from memory and stored in memory. As the metric has a specific“identity” purpose, for example, to represent 10 or 12 years of testscores, it also has a very specific “application” purpose, to decide,for example, if the teacher should be fired. The metric can alsofacilitate a systems needs for further analysis of individual componentsof the metric mean, meanings. As an example, the 5^(th) item pulled frommemory location 5 of 10, of “means”, would be the 5^(th) “year” meaningof the test score, whose value may decide on the firing of the teacherby the system using the metric “Statistics”. Another important point ofconcept to understand, is that the test score mean, is ultimately amathematical calculation without the steps; therefore, so too is themetric value a system process of mathematical steps it represents. Forexample, all values of means now have mathematical equation meaning. Theend result was comprised from a function and an execution of what thatfunction led the system to calculate; that was the final set of resultsstored as means. Metric “mean”, of “Statistics”, now takes on thecalculation of an equation of finding a mean. Another metric couldcontain the values used to find those means also to be stored under“Statistics”. Therefore metrics of the mean could be consideredmathematical process metrics with metric storage locations being themethods used to calculate a mean of test scores. The address locationthen becomes the process, while leaving out any other needs forreprocessing or processing in a system in which patterns need to befound. This makes even none-rule based systems, known as black-boxedsystems in training worlds, rule-based or non-transparent learningsystems. This is so because complex series of calculations are nowreduced to their metrics, leaving out having to recreate the path thatproduced them, and instead providing other parameters that are usefulsuch as learning off the number of elements of the metric itself, whichcan identify pattern likelihood simply due to the frequency of which thenumber of elements populate the metric. The point of the metric, then,is to make portable the relevant information of the process used tolabel an identity of a desired set of values (or calculations or othersub-metrics) by the system using these values. A set of metrics isportable because the metric container can now be stored in a file andtransported to an application for further analyses of a case of data.This is possible as all relevant information of the case data set iscontained in the metrics. This means metrics can be used as a key fordata encryption, and a key of this nature is not crackable by algorithmsas an algorithm is not what created it. What has happened in a metriccreation is that all that is relevant has been placed in memory, and allthat was relevant can now be used as function variables of differingdata types. (All that is irrelevant can also be stored as a metric) And,all that is relevant is decided upon by the system process, whichimplies that all that is decided on can be that in a learning module.This becomes an equation of sorts, because the metrics are tied to thedata case through transformations of the data case data. The metric is acontainer that describes, mathematically, essentially, the patternsfound. Also, the metrics have meaning only to that data case, and in theapplication, only to patterns that define the data case the system choseimportant in the data case. Analysis then, only need be done once, nofurther instances through firmware, due to respective system processes,need to be performed, making this ideal for learning hardwareimplementation. For example, if communications between two individualsmust be communicated precisely, and secretly, the recording of theprimary speaker saying specific sentences (for accuracy improvements) istransformed in to a set of metrics by the micro code or PGA or FPGA, andso on. The metrics can be at a base station to decrypt as the metric isportable. It can even be sent over a line because the data ismeaningless to a person hacking a line as it is just a bunch of uselessnumbers that can never be put together to get anything intelligible. Themetrics that cannot be brute forced to be decrypted as the othertraining data used to create the metrics, would be needed and thetraining process exactly duplicated which is improbable in the maximumsense. So, again, it transforms a pattern of interest into a sequenceequation of sorts, whose elements are clear only to the system thatcreated it, which is the training module.

Contour Map: A mapping of contours that are groupings of values of areasthat have a similar relevance. In geographical sense, it is the verticalelement between distances in an x and y, referred to as the z axis,which are contour maps of elevations. The topographical contour mappingwould result in similar area enclosure of data points. Increasing thenumber of contours then increases the detail of the hilly terrain. Thatsame concept can be applied to a range of data that is found to bebounded by two points in x, and y, having those values represent theelevations. In the preferred embodiment, topographical methods lead tocontinuous lines where the preferred embodiment constrains itself tomatrix separations that can be converted to continuous lines but for thesake of faster processing does not chose to in the transformation of acase to a contour map. Contours can be lines, but the system willenclose those lines by boarders of the pattern of interest, or byneighboring points, to create an enclosed contour surrounding thecoordinate point sets of interest. The points, for example, in a datasetto be used for topographical reasons are contours connecting points ofelevation. This methodology is also used to enclose pixel intensitieslocated at pixel point, x, and pixel point y. The methodology could alsobe applied to any set of values whose point's locations are given an xlocation and a y location, or a higher dimension. This is a veryimportant aspect of the invention, as the contour coordinatepoint-to-point representation has no shape requirements or knowninformation. The metric can be a part of one contour of two differentdimensions within a single dimension as contours in this contour mappingcan use parts of one contour to make up another contour, all through itsmetrics, groupings, and training of the LCIS that makes up thisembodiment.

A Contour of Interest or Pattern of Interest: A contour that looks likea chair may not be a chair unless the contour of a black spot on a wallsays it is a chair of interest. A dynamically changing tumor may not bea tumor of type x unless the combination of contours of a blood clotalso change or maintain shape with this contour. Another example issecurity. For example, the voice peak contours represented by thespeaker taken from a microphone, or even a software cut and paste of agraph within an application package (which has been stored in a systemreadable format), may not be the speaker unless the contours of thenoise background is also part of the peaks. The primary point is to knowthat a contour is a pattern that may not have a user identifiablevisually described form. It is a collection of instances that havemeaning to an identifier through a learning process that uses pastcontours.

Let us now describe a preferred embodiment. We use manifold and contourpattern metric sets interchangeably to mean the same thing, but in waysthat give more clarification. If a top level description is desired toconvey a meaning, manifold is thought best as it is quicker to write. Ifdetails and driving a point home is desired, such as coming right off acontour mapping process, “contour pattern metric sets” is generallyused. If manifold is used, it is usually referred to as a code becausethe metrics within the manifold are being used by a LCIS (LearningContour Identification System) process, in whole or part. First themethod will be given and then the hardware system.

To obtain a manifold, again, a contour pattern metric set, of anydigital image file format (types: raster image formats, pixel and Webfile formats, meta/vector image file formats, bitmap file formats,compression file formats, radiometric file formats (temperature andimage), GIF image file formats, animation file formats, transparencyfile formats, interlaced and non-interlaced file formats, JPEG imagefile formats, and progressive JPEG file formats) one requires theprocess of FIG. 1, Steps 10 through 17, with 18 formatting the outcome.

The goal of the process of the invention is to identify each contourpattern, and background contour patterns, within a data format,according to manifold multi-groupings (FIG. 1, item 13), or singularitymanifold (FIG. 1, item 12) grouping thresholds. These can be determinedby look-up processes (each intensity value is its own manifold so itimplies searching for all unique intensities and then enclosing);determined by spacing distances (same intensity, separation by differentintensity, or ranges); determined by randomly chosen ranges (fastcontour pattern searches can remove manifold patterns if training setstates that manifold is of no interest, or of interest, so randomlyguessing can enhance decision in choices of what combinations of 12-15of FIG. 1 to perform); determined by training or look-up tables by anyprocess that has learned through the training of past data formats ofsimilar or dissimilar contour patterns, also manifold identified throughFIG. 1, 10-17, relative to the current source file, used in FIG. 1(examples are: Machine Learning, Data Mining, Neural Networks, and so onas given in 18, FIG. 1.); determined by classification methods (decisiontrees, statistical processes analysis, such as statistic models such asGaussian Mixture Models (GMM)); determined by reclassification of re-useof invention FIG. 1 through iteration and eliminations of manifoldsgenerated by FIG. 1, generated through a feedback or adaptive filterprocess as stated in 18 of FIG. 1.

The choice of manifold size, shape, or distance between same, with theinvention of FIG. 1, is determined by single, or combinations of,statistical analysis routines (Gaussian Mixture Models for one example),classification routines (Classification and Regression Trees (CART) forone example), past training of past data for future predictions (MachineLearning training on past data or known data format data), and feedback(Adaptive Filtering of data, for one example). For example, FIG. 7, FIG.8, FIG. 9, all use the process of FIG. 1, 10-17, in a (patentapplication by same inventor of FIG. 1, to be applied for, if necessary)GMM, Tree, Machine Learning, Adaptive process hybrid algorithm, given in18, of FIG. 1. By using FIG. 1, application embodiments of FIG. 1 can bewitnessed “currently functional” in FIGS. 7 through 9. The examples ofthe different file format types of thermal data, signal data, and imagedata, through one process, is possible because each manifold isrepresented by its own set of points by way of FIG. 1. Contour patternscan, therefore, be removed from the digital image file format andidentified away from the source file as an individual contour patternwith metric named identity and shape closure repeatability. Withclassification trees, each contour pattern can then be combined withother manifold contour patterns, according to a training set the treewas created by, in the feedback process. If the tree could not classifythe contour pattern, the adaptation process (18, FIG. 1) changes theFIG. 1 choices in 12-15, and re-tries to classify the contour patternthrough GMM and Tree classification. Also, because of this metricsdefined manifold, “each” manifold can be metrics manipulated to showarea by filling in the manifold with weighted unity values that can beused, for one, to represent density. If manifolds are filled with one's,for example, all ones (1's) in the columns, defined by the y-axis, aresummed along the x-axis so that a density representation can be found inthe x-axis of this bin like histogram. If the same is done in they-axis, where column are those of the x-axis, a density or probabilityof identity, of the contour pattern, can be created for statisticalidentity comparisons in the Tree feedback section of 18, FIG. 1.Performing this action then, can simplify the classification process of18, FIG. 1, because if the density (probability distribution created byfilling the manifold and summing along x-axis and y-axis) is Gaussian,there are two sets of means and variances that can represent eachcombination of these manifolds, or their single manifolds. And, asGaussian distributions can be handled as linear additions of means andvariances, each Gaussian distribution can be a sum of Gaussians, so manymanifolds that are Gaussians can be combined into sums, or removed fromtheir sums in a training system process. Other statistical models havelinear statistical combinational characteristics, but Gaussians are morefrequent in nature as stated by the Central Limit Theorem; still, theytoo, however, can be used along with Gaussians in the feedback process.This means that many sets of means and variances, in both x and y, canbe used and sent to a tree classifier for complex identification, not tomention the additional use of areas calculated from the manifolds, andthe equations of the lines generated from the manifold's coordinatepoint set identification.

In FIG. 18, for example, detection of a signal, while enclosed in thenoise envelope, was still found. This is essentially impossible in otherpattern identification methods or other image detection methods. Also,because this manifold filling (for density and area representations in18, FIG. 1), of the generated manifolds, scaled or left unsealed,creates a density value, area value, magnitude value, and location valueof the contour pattern (as well as many sub-level equations, of the sameformat, depending on the number of manifolds you create for the desiredlevel of identification), a signal now takes on a very detailed metricsdefined fingerprint description. Taking empirical data (although randomsimulation data can be used in the same way once plotted by graphicalsoftware, whose image is then stored in a digital file and sent throughFIG. 1) and identifying in this manner, gives the operator much moreinformation about the signal that currently requires Fourier analysis.This Fourier analysis type of analysis can now be avoided, which meansthat FIG. 1 is a new form of performing a metric coding process in whichto evaluate any electric signal type, or the like, more accurately than,in many ways, than that of Fourier analysis, as Fourier analysis cannotachieve the same results of detecting a signal in noise as FIG. 18demonstrates. This process of FIG. 1, 10-18 is clearly will change howdata will be used in analysis as no math process is able to assignmultiple levels of {density, area, coordinate point set enclosure, andequation of lines} to each and every data point or cluster, as FIG. 1,demonstrated in FIGS. 7, 17 and 18.

FIGS. 7, 17 and 18 show that, regardless of the data file format, notonly can a spline be created to increase the number of points(smoothing) given by FIG. 1's manifold, an equation of a line throughthe manifold points can be generated by FIG. 1, and a metric created forit, as well as doing the same with a density value (a probabilitydistribution equation).

By way of FIG. 18, detection of communication in some jammed channelscan be detected; that speakers in multi-speaker environments can bedetected and identified (for example, in the combination of GMM,Manifolds, Trees, and Adaptive Feedback Machine Learning, of a knownspeaker, FIG. 1 can be used to match speaker voice, to speaker imagecontour pattern); that recorded or real time discussions can be decodedin encrypted data sets; that images of underwater contour patterns canbe detected from ultrasonic echoes; that contour patterns can beidentified and detected in electromagnetic imaging's of healthcaresystem hardware such as MRI's, ultrasound, and other image detectionprocesses; that detection and classification of signals and contourpatterns can be done in communications and image capture systems(thermal, electromagnetic, ultrasonic, laser, and so on) in militarysystems (and that all can be tracked, dynamically, as well, withoutchange to the algorithm); and that strokes, heart attacks, or biologicaldiseases can be located in living bodies. And, for all of these, ifbefore and after is done, changes can be identified by FIG. 1's, 18preferred embodiment, as it learns from past data.

The process of FIG. 1 is data format independent as all data can betransformed to an intensity image set. FIG. 7, for one example, is athermal image of a contour pattern. Thermal radiometric file types arean entirely different representation of data as the container includes aset of just temperature changes (as displayed in to the right of 31 inFIG. 7) mixed within a JPEG file format container that also contains theimage 30, FIG. 7. Through the process of FIG. 1, and using 18, FIG. 1,the simulation of FIG. 1 shows that in any Statistical Analysis (of thefiller of a manifold), classifier (of the statistics generated byStatistical Analysis), Machine Learning (past history training),adaptation process (feedback or adaptive filters), an contour patternidentification can be performed. The result being a complete, user only,limited level of fingerprinting, capabilities. And with the levels ofmanifold defined metrics representations, contour patterns can betracked real-time (Video).

Video, as it is simply frames of data, is no more of a challenge to FIG.1 than any other file format type.

FIG. 7, FIG. 17, and FIG. 18 are actual results of using invention FIG.1 and the LCIS system, in 18's preferred embodiment. Also note thataccuracy is not limited by the data format type, it can actually enhanceit if one is cleaver.

The greater the resolution of the display device used in the file type,and then used by FIG. 1 to convert to a manifold, the greater thedescription of the contour pattern in terms of density and manifoldshape. Therefore, the choice of the compression routine can increase ordecrease detection accuracy and precision and therefore, can be used asanother means of zeroing in on the contour pattern classification in 18,of FIG. 1. This implies that data format type is another means to adjustthe manifold in contour pattern shape (in manner of increases, or inmanner of decreases, in all concerned implications of FIG. 1), or indensity (from weighted fills or identical numerical fillings of themanifold found in FIG. 1), or in its point data set, or in its area,and/or in all sub-metrics representations of the first level that FIG. 1provides the user for metrics analysis due to single or multipleiterations of FIG. 1 of the LCIS described by FIGS. 8 through 16.

Another example of changing the accuracy of the detected and transformedcontour pattern is windowing the contour pattern frame processed withinthe image, or within the window of the data being captured by hardware.For example, in signals, or clusters of heavy density (FIG. 1 can createmanifolds of clustering densities—from weak to aggressive—so categoricaldata can be analyzed by invention FIG. 1, as well), it may be desired toexpand out a time window of 10 seconds to 1 second so that the resultspreads out the pixel density over a larger area, which then changes themanifold metrics representations to a larger manifold, or to a set ofsmaller manifolds (all under one manifold, if desired). These newmanifold reads can be determined and calculated all through iterationsof FIGS. 1, and 8 through 16, which can also be linked to the nexthigher level of the past manifold math representation created beforeexpansion. For example, in FIG. 18, the electrical response ringing'sfound at the peaks and valleys, which cause the bright spots in 170,shown by arrows, are identified and shown visually by manifold patternsaround a center ring 167 expanded in FIG. 19. In this case, the x-axistime, at one location peak point, in 170, could be expanded in both time(x-axis) and amplitude (y-axis) to focus only on the peak and time stampinvestigated; that is, zooming in on 170's image point, represented by162 in FIG. 18, is being expanded and re-evaluated by FIG. 1 and FIGS.8-16. This action can be done at a multi-dimensional (2D, 3D, and so on)level as well. This action would change the manifold shape so that themanifold would have multiple metrics representations for just one singlepoint. The benefit contour patterning is to thin the empirical densityresults to determine different sets of manifolds that may furtherfingerprint the contour pattern it identifies. This enhances 18's(FIG. 1) embodiment's ability to remove noise from images looking forspecific contour patterns. It should be understood that keeping adensity package tight, however, can reduce needless calculations ofempirical results that frequently hit within a contour pattern metricset. This means that a checking routine, that verifies whether or not apoint is inside a manifold or not, can be used to remove a process ofevaluation of points, within the manifold, and instead process onlythose outside of it (or vice versa). This can reduce calculations madeon repeatability calculations (points hitting continuously in one spotor area) within a manifold, or enhance error analysis of empirical databy establishing a bound, by way of FIG. 1's manifold's processedcoordinate point set edge. It does so as a range of values that can beprocessed by FIG. 1; that is, it is then assigned as the manifold sothat, unless an evaluation is outside the range, only one value contourpattern within the manifold need be processed by FIG. 1. This strengthof this manifold analysis, on empirical or theoretical data, is why thiscan be considered somewhat as a new way of paralleling math kinds ofcalculations without actually doing math; that is, the results of themetrics representation in FIG. 1, 18, can be used without violation ofthe rules of algebra.

In the GMM, Manifold, Tree, Adaptive process, the adaptive process canadjust the window so that better detection to the training set can bedetermined. This windowing opens up all sorts of new uses for themanifold as one manifold of a contour pattern can be represented by manyother manifolds, giving the classifier more information to decide on.Each manifold not only has its own density, its own set of points, itsown probability, it also has its own set of further densities, and thelike, due to iteration processing of FIGS. 1, and 8-16. The manifoldcontour pattern metrics identification is only as shallow as the userwishes to go. (Note: Noise (determined to be unwanted manifolds) can beremoved at each level of iteration of FIG. 1.) Clusters, for example,can result in a manifold ring around the tight clusters, as well as amanifold ring around the tight and loose clusters, all processed byFIG. 1. Analyses of methods (18, of FIG. 1), already used in FIGS. 7, 17and 18, can represent one cluster as two probability distributions, oftwo totally different manifold metrics representations of 18, FIG. 1.And, because the manifold patterns can be increased or decreased, thecluster can have many more than just two, in these examples, as well.This ability can help eliminate cluster overlap as the overlap ofclusters can also be a manifold, which can be subtracted and added; andin the case of Gaussian Mixture Models, as the sum of two Gaussiansrandom variables, is Gaussian by convolution, densities of manifoldsthat are Gaussian can be added together to create another mixture ofGaussians. For example, in FIG. 18, the inventor not only was able toremove the signal from the noise, but he also had complete control ofthe noise envelope; meaning that he no longer was tied to the imagesource as the image was completely, metrics, ID'd. Therefore, thesethree FIGS. 7, 17 and 18 are an example of overlap handling thatinvention FIGS. 1 and 8 through 16 can obtain in a manifold filler (FIG.12, and FIG. 14), feedback (FIG. 10 and FIG. 11), statistical analysis(FIG. 15 and FIG. 16), learning contour identification hardware (LCIS)process (FIG. 1, FIG. 8 through FIG. 14).

FIG. 1 makes grouping of metrics expressions, possible, without havingto worry about algebra mistakes as the metric is the code that describesthe pattern decided by the LCIS system to be what is relevant. This hasnever been done before now, and the LCIS computational complexity isalmost negligible. Again, it is in practice at this time, anddemonstrated in FIGS. 7, 17 and 18 as examples working just as stated inthis invention application.

To describe a simple and general embodiment of the process of creating amanifold with the steps, which yields a complete metrics expression ofeach and every contour pattern in a contour mapping of a test andtraining cases, including noise, the following description of FIG. 1process is presented as a series of steps as used by the LCIS systemFIGS. 8 through 16 and supporting FIGS. 2 through 6.

Step 1.

FIG. 1, 10, read in a digital file from a storage device (or from acapture device using cameras, scanners, or screen captures, or thelike), having one of many graphic formats. (A few format “type”examples: raster formats, pixels in Web formats, Meta/Vector formats,Bitmap formats, Compression formats, GIF formats, animation formats,transparency formats, Interlaced and Non-Interlaced GIF formats, JPEGImage Formats, Progressive JPEG.) All work the same way to the processof FIG. 1. FIG. 1, 11, is used to develop the intensity matrix thatrepresents the image in the source data format. Item 11, in FIG. 1, ismaking each intensity value its own manifold enclosure at this point inthe process.

Step 2.

Obtain from the loaded data format (FIG. 1, 10) the graphic intensityvalues (FIG. 1, 11). These can be color shades, or black and whiteshades of intensity values of any bit length. Having the loaded file, amatrix of intensity values can now be represented as a row and columnmatrix as shown in FIG. 2. In FIG. 2, an example is formulated thatrepresents a simple image intensity matrix as required by FIG. 1, 11 sothat all manifolds of the image may be processed through 12 through 15,of FIG. 1, for reduction. The example created represents two images ofline contour patterns, of two heights, and one image of a contourpattern, which is square.

Step 3.

Determine the minimum and maximum value of the intensities. In FIG. 2, asimple 5 by 8, pixel image simulation, is presented. Here the maximumand minimum values are 5 as the contour patterns in the image aredefined by intensity values of 5. In reality, these values will be realnumbers such as 5.663121234234 (as a quick example of a real number),depending on the decimal point of interest, and determined by thresholdsset in FIG. 1.

Step 4.

Define the manifold, or enclose contour pattern, by a set of points thatdescribe the boundary of the contour pattern. FIG. 3 represents thechoice on one manifold ring (12 and 14 or FIG. 1). It is calculated thatthe distance between 1 and 5, in the matrix location space, is one-halfa unit between the points in the matrix. In FIG. 3, you see threemanifolds defined by 19, 20, and 21. For example, manifold 1 (19) isdefined by points (x,y) as set {(2, 1.5), (1.5, 2), (1.5, 4), (2, 4.5),(2.5, 4), (2.5, 2)}. Manifold 2 (20) and 3 (21) are defined in the samemanner.

Assume that the 18, FIG. 1, preferred embodiment, claims that themanifold is consuming too much space (or area) around the contourpattern depicted by intensity value 5 (an algorithm in 18, FIG. 1, cantake advantage of this ability to iterate the process of FIG. 1 (FIG. 10and FIG. 11) by using more manifold patterns (FIG. 13 and FIG. 15, andFIG. 19) to increase identification of the contour pattern, andtherefore, to create more sub-metrics identities of resultingmanifolds). To reduce the space between each manifold defined ring, thespace between intensity value 5 and its neighbor 1, for example, mustdivide the space between x-axis markers, 3 and 4 (22, FIG. 4), into moreintervals. FIG. 4 and in application use, FIG. 19, shows this processresult of using two manifold patterns. Take note that matrix I, FIG. 2,does not change in this process as the image file does not change. Alsotake note that the representation of the manifold enclosure point-setdescription is a division of the x and y axis, (FIG. 15 and FIG. 16) andso the manifold is “transforming intensities” to x,y-axis locationvalues, within the matrix rows and columns.

The space between location 3 and location 4 (22), in FIG. 4, is nowdivided by two equal parts. The effect is the shrinking of the areaaround the image intensity values of 5 (FIG. 19 of action of FIG. 18),which has the effect of more closely identifying the contour patternarea (the error in area being in the resolution, or pixel spacing byincreasing contours in 87 through 88 of FIG. 11). If we continueshrinking the space between the location of intensity value located at(x, y) coordinate, (2,2), and intensity value located at (x,y)coordinate, (1,2), the center manifold begins to fully enclose andreduce the manifold—reduce area, with the density within a filledmanifold being minimized to a bound—of the intensity values located at(2,2), (2,3), (2,4) of 23, FIG. 5 (producing, possibly, a shape as shownin 166 and 165 of 167 of FIG. 17).

FIG. 5 is used to show multiple iterations of dividing the region upinto 20 manifold patterns; that is, for manifold 1 (23), 2 (24), and 3(25), 20 metrics representation sets (as given in 18, FIG. 1) have beencreated (again, best shown by FIG. 19 through FIGS. 8 through 16).Again, matrix I, in FIG. 2, will not change. Invention described in FIG.1, is just dividing up the space between the intervals 3 and 4 so thatmore patterns of manifolds can identify the contour pattern at location23, 24 and 25 (FIG. 5), or to reduce the area of the contour patternmanifold to a closer approximation of the contour pattern area space.However, it is important to note that the fill of the resulting manifoldreduces the “area” to a more accurate description of the contour patternand it also determines the minimum “density” of the contour pattern(more on this below). These density values are important in manifoldfills (145 of FIG. 16), as a minimum probability density curve will beconsidered, and proved (FIG. 10 and FIG. 11), to be the lower bound ofthe contour pattern the manifold metrics representation represents.

Step 5.

The desire may be to throw out, from consideration, all manifoldpatterns but the center patterns of FIG. 5 (23, 24, 25) (example wouldbe to throw out contour pattern metrics sets of FIG. 18 having lesspattern rings than given in, say, FIG. 19). This leaves only the centerpattern manifold metrics coordinate point-to-point representation forthe fully defined manifold and therefore, could represent a “point area”of metrics classification (116 of FIG. 14) of the contour pattern to anyapplication using the manifold created by FIG. 1 and FIGS. 8 through 16.That is, in FIG. 5, then, four manifolds (the three center patterns, andthe background of 1's.) would then have been created for use in anapplication instead of 61 manifolds (3 times 20 manifolds plus onebackground equals 61). This reduces computational complexities foralgorithms using the benefits of manifold creation and it is alldetermined by FIGS. 10 and 11 through FIG. 13. For these centerpatterns, then, it can be said that the manifold resulting from thedetected contour patterns will approach the exact shape (increasingidentification accuracy in FIG. 11) shape of the contour pattern as thenumber of divisions approach infinity—in this example, then, to thepixel level (or to the data format levels limits) of the data formattype, as given in Step 1.

As it is shown in Step 4, the points completely defining the center ringare calculated by FIG. 1 and the LCI process of FIG. 13 made up fromFIGS. 8 through 16, and as the divisions are proportionally spaced(although not necessary), the divisions take place at a known point ofreference in the matrix (not in the intensity values), creating thetransformation of intensity values to an contour pattern location value.It is important to note that all manifolds, in this example, are all aretreated in the same way in the coding of all manifold points to berepresented by the matrix (FIG. 14). This implies that a closed manifoldcan enclose a contour pattern that has little area, that is a “line,” or“point,” in any file format. Therefore, manifold accuracy is defined,approximately, by the pixel width and height of the data format used inStep 1. Again, to verify the claim to this ability, visit the manifoldsin FIG. 5. There, both manifolds found, and described by points of themanifold, have an enclosed manifold of a line of area basically definedby stacks of pixel resolution widths (see manifold 1 (23) and Manifold 3(25) of FIG. 5).

FIG. 5 also shows that the process of invention LCIS can describe adepth (density) or area, as described in manifold 2's square contourpattern (FIG. 5, 24).

If the manifolds are filled (part 2, of 18, FIG. 1) with ones (replacingall intensity value locations in FIG. 2, with the value 1; remembering,the manifold leaves the data set so the background of 1's do notinterfere as the manifold is defined already), the density of 23, inFIG. 5 would be calculated from a single histogram stack height of 3along the x-axis, and 1 and 1 and 1, in the y-axis. For 24, in FIG. 5,the density would be 3 and 3 for the x-axis, and 2, and 2, and 2 for they-axis. For 25, in FIG. 5, the density would be 2 in the x-axis, and 1and 1, in the y-axis. In a real image (145 through 155 in FIG. 16),these densities are likely to form a histogram distribution similar towhat is described by the Central Limit Theorem, which is normal, orGaussian. Also, using splines, more points around the contour patterncan be created, and then scaled to give an entirely new density (canstore as another metric by the LCIS as 116 in FIG. 14). Also, take notethat the fill does not have to be 1's, it can be a weighted value timesone, called unity weighting, if the user wants to give more meaning toarea. In fact, the density fill, does not even have to represent adensity. It is an identifier, after all, nothing more, but it is best asa density as it has Statistical meaning.

In FIG. 3 through FIG. 5, the values of intensity represent each of thecontour patterns as the values come from the format of the digital file.In the case of FIGS. 3 through 5, there are two contour patterns thatrepresent “lines”, and one contour pattern that represent a “square”.Another embodiment is to combine a manifold of different contourpatterns using the same technique described above, but which now takeadvantage of combinations of two or more contours (84 of FIG. 11, or 83through 87 of FIG. 11 with FIG. 10 to assist in FIG. 13). For example,in using FIG. 1's 12 through 15 choices, combinations of manifolds canbe created to create another manifold metrics expression.

In FIG. 6, I, in FIG. 2, will be redefined to include differentintensity values to indicate four totally different contour patternmanifolds that are available to FIG. 1. Each value added is now a realnumber, rather than an integer of 5, to indicate that ranges can bechosen by rounding methods of real numbers; that is, precision is beingcontrolled by manifold selections.

In FIG. 6, the process was given a threshold in FIG. 1. The threshold isto combine groups of contour patterns in intensity range 4.5 to 5.22(122 through 130 of FIG. 15). The contour patterns in this set are nowdefined by 11, of FIG. 1, to be set {1, 5, 4.5, 5.22}. The threshold ofspacing stayed at one to isolate the two contour patterns from oneanother. Manifold 1, for a quick example, is defined (by 18, FIG. 1) byy-axis points {2, 2.4725, 2.4725, 3, 3.5275, 3.5275, 4, 5, 5.4725,5.4725, 5.4725, 5, 4, 3, 2, 1.5, 1.5275, 1.5275, 2} and Manifold 2 isdefined by set points {2, 1.5, 1.5275, 1.5275, 2}. The x-axis is simpleas well. An pattern of the points (or smoothing using spline mathematicsto interpolate more points, for example) going through these pointswould represent the metric of the manifold enclosing the contour patternof interest (139 of FIG. 15 and 113 of FIG. 14).

The value of contour pattern intensity 1, is, the background, which isdecided to be a shell of the contour patterns contained in the wholeimage. The image resulting from subtraction of the individual manifoldscreated by FIG. 1, would represent cookie cutter remnants that wouldgenerally be defined as noise, or information of no interest (the singlecontours found in FIG. 18). This noise, through manifold filling, anddensity calculation's from the fill, can still be valuable to analgorithm needing to adjust to the noise intensity of the image. This isvery important for Statistical, Feedback, Classification, MachineLanguage trained algorithms (or combinations of) as removing noise fromthe contour pattern can be very valuable as seen in the FIG. 7, FIG. 8,and FIG. 9's results of FIG. 1. Noise is not thrown out, and does haveuses so it's manifold, point-to-point, and metrics representation (117of FIG. 14), is of importance just as the intensity values are,especially in multi-speaker identification uses that was used in findingthe result shown in FIG. 18.

It is clear by FIG. 6's final calculation of manifold values (example ofy-axis values {2, 2.4725, 2.4725, 3, 3.5275, 3.5275, 4, 5, 5.4725,5.4725, 5.4725, 5, 4, 3, 2, 1.5, 1.5275, 1.5275, 2}) that the pointsdefining the contour pattern (113 of FIG. 14) do not indicate themagnitude values of 5, or 1. The points are instead, intensity to matrixlocation, “transformations” of FIG. 1's final 16 and 17's processes.That is, they are simply division's point locations of the separationsbetween intensity values in the matrix (124, 126 and 129 of FIG. 15).They represent single or combined groups, of separation change values,from intensity pixel, to manifold wall (FIG. 1, and 64 of FIG. 10, and83 through 87 of FIG. 11, and FIG. 12). If it was desired, a choice of13 and 15, in FIG. 1's process, can process another threshold that makesmanifold 1 and 2, in FIG. 6, one contour pattern, as well.

Combining contour patterns in this fashion creates sub-code manifolddensities (117 of FIG. 14) that can be used for classification algorithmanalysis. The beauty of process 10 through 17 is that no changing to theprocess machine code is required for the finding of the manifolds, forLCIS systems that wish to use it in processing (18, FIG. 1). Thebenefits of FIG. 1 are that combinations of Statistical Analysisroutines, feedback, classification, and learning contour identification,would not be possible if it were not for FIG. 1, FIG. 13, and FIG. 14(of course other figures in support of these as well). Computationalcomplexities in today's current technology do not allow for the removalof the contour pattern identified in the image, as they find no need tocreate a metrics identity to the contour pattern it defines. Thisdefines the novelty of the learning contour identification processes. Itfinds all contour patterns down to a single intensity point, andrepresents it by an area that is transformed through manifold filling,to a probability density value representation, and area, a location, andan metric container (FIG. 14) that can be changed in shape like aballoon; that is, the points (113 FIG. 14) represent an area and anyarea defined metrics can be changed into another shape having the samearea like a squeezed balloon. All this is done in terms of defining themanifold by a metrics of information FIG. 14.

Step 6:

Finally, Steps 1 through 5 perform the steps of finding all contourpatterns within the image. Step 5 takes advantages of thresholds todefine groups of contour patterns if the user, the classifier, thefeedback system, or the statistical analysis, or combinations of,desires. Step 5 allows the operator, or process of 18, FIG. 1, todetermine a range of divisions that one desires (See Step 4) between thelocations of the contour patterns (122 through 138 of FIG. 15 and 140through 160 of FIG. 16). It is a simple weighting of the divisions ofthe spaces as shown by the gradients in the figure. This step, Step 6,then, is to take advantage of the transformation of contour patternshape, to a metrics representation that is of set (18, FIG. 1){probability density, area, x-axis location, y-axis location, sub-areas,sub-densities, sub-axis locations, and sub-y locations} and store asappropriate in 113 through 117 of FIG. 14.

One performed embodiment of FIG. 1 is to locate cancer cells. FIG. 17,represents a source file of cancer cells taken by microscope, and thenplaced in a digital image file container that is TIF. FIG. 17, 161through 163, is used to identify and remove from the environment a setof points that cannot only be plotted on an x-y axis, but can be used ina classification, feedback, statistical adaptive process by way of themanifold code that is linked to 165 and 166, emphatically. FIG. 1 andFIGS. 8 through 16 created the image outlined in 165 and 166, but togreater detail than just an outline of the image, and as two manifoldsthat reproduced the exact shape, but chose portions of the original thatmore clearly link the cell to exactly the one of interest which is thatfound in complete form, than half removed from the image (161). Fillingthis manifold, then, lead to very detailed density, statisticalrepresentation of the EXACT type of cancer and can therefore be used intracking algorithms that use the main embodiment algorithm of GMM,Machine Learning, Decision Trees, and Adaptive feedback systems. Thisanalysis is an actual implementation of the said process and LCIS use ofFIG. 1 and FIGS. 8 through 16.

Another embodiment is that of signal waveform analysis, common incommunication signal systems, security communications system,electromagnetic wave receiver systems, encryption systems, and so on.FIG. 1 is used in FIG. 18 to show that a signal in noise is detected.Although not shown in FIG. 18, a complete description of the noiseenvelope was also found by 18, of FIG. 1 executed through FIGS. 8through 16. It is the lighter shade surrounding the signal in 171. Theprocess in 18, of FIG. 1, is that of creating a manifold to use in a GMManalysis, to be then classified by decision trees. The decision treesare built from the Gaussian Mixtures of mean and variances which havebeen determined from the filling of each manifold FIG. 1 created anddemonstrated in 145 through 155 of FIG. 16. This is why there aremultiple patterns on the peaks, and single manifold patterns on thenoise in the background; that is, detection was found and adaption tothe contour pattern finding was now happening (64 FIG. 10 and FIG. 11).Basically, the GMM uses the density calculations of the filling createdin FIG. 1, 18 to determine if the manifold has Gaussian distributed datain x and/or y-axes. The next step, determined from the learning, is ifthere are other created manifolds that are indeed the contour pattern ofinterest in the training set. If there are not, the process stops, ifthere are, it continues to adapt to the manifold patterns (or count themanifold patterns would be an alternative use: FIG. 5 and FIG. 18).Regardless, these actions are used to change the threshold in FIG. 1's12-15 decisions and are a direct result of a learning contouridentification system trained to past data (FIG. 10); also determined bya feedback process (FIG. 11) (that is, adaptive processes tied toclassification that change the threshold through multiple iterations ofFIG. 1)

Although there are many application uses of the processes described bythese figures, as the outcome is entirely metrics and removed from thesource, there are no changes necessary to FIG. 1's process in any of itsuses as they are used in these figures. In fact, this is especially soof the use of LCIS, Statistical Analysis, and feedback, classificationsystem processes that is using FIG. 1's manifold creation process. Thisparticular process (FIG. 1, 18) proves so successful using themanifold's metrics findings, 18, in FIG. 1, can even be used to locate aline of unit one, pixel, thickness. FIG. 7 is an example of thiscapability.

The thermal image in FIG. 7, 30 shows the radiation pattern picked up bythe hardware device that stored the image as a radiometric data format.FIG. 7, 32, shows that through Statistical Analysis, feedback,classification, and re-processing the contour pattern metrics of FIG.14, created by FIG. 1 and iterations of FIGS. 8 through 16, each time,reduced 19200 possible point set metrics that are disconnected to 4044point set metrics that give a contour of exactly that which is a truepattern of interest. Then, through the feedback, classification,statistical analysis processing of density values and areas, the LCIS ofFIGS. 8 and 13, created the image of 33, in FIG. 7. This image in 33 isone manifold stream of data points output by the display device 110 ofFIG. 13, by 101 application module LCIS of FIG. 13. To a classificationsystem, the density of this final manifold is then stored away from thedata set it was taken from (111 in FIG. 14 stored as a file formatnecessary for the user of 101 in 13). Now, not only can Machine Learningtake advantage of the density, areas, and x,y-axis locations, that areassigned to this end result, but it can also take advantage of itssub-level manifolds in 32 of FIG. 7. The result, then, is a completefingerprinting of the chain. If the user wishes, many levels ofsub-density values to 33, each iteration of the manifold(s), in thefeedback/classification process, can be stored and linked to this finalmanifold representation of the image. If a tree classification system isused, it is clear that procedures like Gini Indexes in Classificationand Regression Tree algorithms can be completely replaced by actualprobability distribution values of the contour pattern metricsthemselves.

The system of the process just described begins with a background of thecomponents. The LCIS, of FIG. 8, and FIG. 13 are now discussed.

Learning is a process that uses statistical methods to create learningtechniques whose processes are single or multiple iterations ofexecution of machine language coded algorithms (55 through 72 of FIG.10). As a system (FIG. 8 and FIG. 13), as described herein, it is alearning object identification system (LOIS) where these codedalgorithms are processes which take information from memory stored datasets (39 of FIG. 8), of past events called the training set (56 of FIG.10), learn trends and patterns from this information (FIG. 10. 65through 72), and then apply what has been learned to finalize an outputof a new data set (74, of FIG. 11), generally termed the test data. Thestep preceding the final process of the LOIS is generally to identity anunknown event (88 of FIG. 11), or the object of the test data, byapplying to the data the learned trends from the training set. The finalprocess of the LOIS is to classify, store in memory, and display theoutcome, which is to say, to label the outcome as some object ofinterest. Common terms used to describe such systems are those whichencompass field interests of Machine Learning and ArtificialIntelligence research.

The LOIS system generally consists of five components and its firmwareor system(s) software (items 34 through 39 of FIG. 8). A processor getsinstructions and data from memory using a system's data path. The inputblock writes the data to memory and the output block reads data frommemory for the purpose of displaying, or to be further analyzed byanother LOIS. The control block (34) sends the signals that determinethe operations of the data path (35), the memory, input and outputblocks, 37, and 38. The control then sends the data back to memory, andto a display device (110, FIG. 13). The processor contains thecontroller and has a data path to memory. The system is controlled bybinary machine language program processes which can be transformed intoa higher level called the assembly language program, or even further, toa high level language that is user and application development friendly.In all cases the coding is the process that makes the system work inunison with parallel or serial versions of the same LOIS systems. Thisimplies that a system can contain blocks of other systems each havingexactly the same set of hardware components (100 through 110 of FIG.13), each performing a different action, or a process in unison tobenefit one or many of the other blocks that may be serially or paralleldesigned into said system. This is defined herein as grouping of systemsor a grouping of independent processes having their own basic assortmentof controllers, memory, input and output, and data paths.

Generally, input data format changes occur in the processor of thesystem and does so for the purpose of controlling another processor inthe system. This is done so that the sequence of system-to-systeminternal operations provide a final output to storage if training todata (display of data is optional) 106 and 111 of FIG. 12 and FIG. 14,or to storage and display if testing data is the LOIS. Translationsoutside of the LOIS, to the same initial input, are considered done byanother system attachment of the same makeup or a makeup which issimpler in whole; that is, memory may not be necessary. Transfer of thedata is done via data value-to-bit translations of the processed data.These data format changes can be a result of a specific sequence offirmware machine codes, higher level language application softwareconverted to machine code for the processor, or hardware arrays ofelectronic components such a programmable logic arrays (PLA) which havea set of AND gate planes and OR gate planes that are combined to producea specific output of instructions. The hardware can be chips used toimplement a Boolean function, or process. Real complex LOIS systems usedfor designs that require methods of re-processing, iterations, orsimply, multiple LOIS systems are called layers, or abstractions, whichis a technique for designing very sophisticated computer systems.

Typical data sets for LOIS systems are comprised of data sets containingpixel intensities, where each intensity has metrics of axis identifiedpixel locations, and color intensity values where the axis identifierscan be of higher dimension. The term metric, is a standard ofmeasurement to define changes in a data set that were a result of thephysical natures of the device used to capture the data.

Memory of LOIS systems can be inside the processor (FIG. 8, or as givenin 106 and 109 of FIG. 13), stored on some portable media or medium, orindependent of the processor but on board the LOIS system. The access tothe data is by datapaths (35). Memory may be volatile where informationstored is lost when power is removed or nonvolatile memory that is notsubject to power loss such as a magnetic storage device.

Communications between the components and other systems is performed byway of the datapath bus 37 and 38 of FIG. 8 and as given in FIG. 13.Sequences of binary bits travel these paths to provide data andinstructions to the LOIS. If data is not in the proper format the systemcan also provide that action to convert its input into the necessarysequence of bits readable by machine code.

Computer words are composed of bits allowing words to be represented asbinary numbers. The LOIS takes advantage of this ability so that it mayinclude input that is represented by numbers, arithmetic algorithms, andhardware that follows the algorithms in terms of instructions sets. Theunit of hardware that works with bit words is the Arithmetic logic unit,or ALU. It operates by the Arithmetic-logical instructions to processthe math common in the learning phase of LOIS.

Typical algorithms, in the context herein, are rule-based algorithms orblack-boxed algorithms (107 in FIG. 13). Rule-based algorithms aremachine coded processes such as decision trees, where the outcome isanother process from a sequence of decisions that are recorded to orhardware. Black-box algorithms are algorithms whose outcome is hiddenfrom the user, such as a Neural Network.

Hardware to software interface typically is a page table implementation,together with a program counter and the registers. If another LOISsystem needs to use a processor, a state has to be saved (112 and 118 ofFIG. 14). After restoring the state, a program can continue from whereit left off. This saving of states allows the LOIS to save data inblocks. This allows the LOIS of this program to group processes in onelocation to be retrieved in one continuous read. For example, if agrouping of data needs to be held together as one definition of anobject, regardless of length, then it can be done by saving a state. Youmay also append to an area of this nature because you have the savedstate and know here it was place in the process sequence. Therefore, theprocess's address space, and hence all the data it can access in memory,is defined by its page table, which resides in memory. Rather thansaving the entire page table, the firmware operating system simply loadsthe page table register to point to the page table of the process itwants to make active. An example, say one enclosure as described by FIG.14, is a vector or matrix set of points defining a circle on anx,y-axis. The process would save the state, start writing the data tomemory, continue a process, return, start saving summations of rows andcolumns of a x,y-axis data set, continue a process, return, then startsaving statistics of the summations of rows and columns of a x,y-axisdata set, so on. Now, if the state is saved, the table can be called andx,y-points, row and column summations, and statistics can be read as onesequence meaning that after that sequence is read, it can be defined asone data set with dynamic length. In the case of this system, thesequence of data is a manifold. It is important to now that the sequencecan be another set of the same sequence of x-y-axis points, summations,and statistics meaning that any storage sequence can have any length aslong as memory can be allocated for it. This means that a classifier canpull a sequence of any size necessary for learning algorithm needs orfor purposes of classifying its data set stored, or its analyzed dataand manipulating the data stored.

In spirit of the above, the FIGS. 1 through 19 are used to describe theprocess of the invention.

I claim:
 1. A processing system conglomeration for phenomenon andphenomena repeatability tracking, comprising: At least one metric codesequence structure having elements of data format types consistent withmeans for describing, uniquely, characteristics as functions andfunction elements of phenomenon and phenomena, determined by adaptivefeedback tracking system conglomeration processes, for replacement ofelements of original raw data with individual re-processed raw dataelements translated into elements of at least one system readable singleand grouped sets of phenomenon and phenomena metric code sequencedescriptors stored at editable and appendable addresses in a memorystructures not all of same element data type and size of said originalraw data taken by said tracking system, means for assembling from atleast one sample set of raw data, and at least one system conglomerationprocess iteration of said sample set, editable and portable andre-processable, system and portable format stored, phenomenon andphenomena metric code sequences of raw data element group formations ofmeaningful equivalent element formation substitutes of at least onesystem and at least one user input processed through saidconglomeration, as occurring and repeatable in nature and naturalsciences, as phenomenon and phenomena falling into a finalclassification category of at least one of: person, place, thing,experience, expression, idea and combinations of same categories, meansfor communications between stored phenomenon and phenomena code metricsequences and conglomerations of at least one of: interruptible andtiming abled system conglomeration of phenomenon and phenomena codemetric sequence assemblers and code sequence builders, adaptivephenomenon and phenomenon feedback components, system externalconglomeration attachments, raw input data gathering devices, input dataformat translator assemblers as means to process raw data gathered intoformats that are readable to phenomenon and phenomena metric codesequence assemblers, transceivers means for input and output display andedits of phenomenon and phenomena metric code sequence builders duringand after process interruption, input and output devices and systemattachments, storage locations, component conglomeration interruptioncontrollers, and time controllers, and power supplies interruptible andtime-controlled as means for selectively processing elements ofphenomenon and phenomena metric code sequences, system self-testcontrollers, and micro-code processors and builders, means for input tosaid tracking system and data gather devices and means for sending andending component interruptions to same tracking system and itsattachments, means for output of end of processing, final output, ofphenomenon and phenomena repeatability tracking system, means forediting phenomenon and phenomena during, after, and when system sensorsand controllers decide by means for phenomenon and phenomena metric codesequences and user and system interrupts, means for processing at leastone said tracking conglomeration iteration input of raw data andphenomenon and phenomena metric code sequences, means forinterconnecting said listing of means for enhancements of finalclassification meanings to complete phenomenon and phenomena metric codesequences processed, means for interconnecting in a plurality of layersand in higher dimensions of at least one combination of said listing ofmeans for the given phenomenon and phenomena tracking system.
 2. Thesystem of claim 1, wherein phenomenon and phenomenon code metricelements are designed by system designed contour descriptors of at leastone open grouping of at least one element contained in at least onephenomenon and phenomenon metric code storage memory pointer location.3. The system of claim 1, wherein phenomenon and phenomenon are designedby system designed contour descriptors of at least one closed groupingof at least one element contained in at least one phenomenon andphenomenon metric code storage memory pointer location.
 4. The system ofclaim 1, wherein packaged system conglomeration of said phenomenon andphenomena repeatability tracking system is comprising of at least oneof: Phenomenon and phenomenon tracking systems entirely contained in atleast one electronic conglomeration of components needed as means forprocessing and output of at least one phenomenon or phenomena metriccode sequence, Phenomenon and phenomenon tracking systems entirelycontained in combinations of microcode systems and electronicconglomeration combinations needed as means for processing and output ofat least one phenomenon and phenomena code sequence, Phenomenon andphenomenon tracking systems entirely contained in combinations ofparallel combined systems and electronic conglomeration combinationsneeded as means for processing and output of at least one phenomenon andphenomena code sequence, Phenomenon and phenomenon tracking systemsentirely contained in combinations of series and parallel combinedsystems and electronic conglomeration combinations needed as means forprocessing and output of at least one phenomenon and phenomena codesequence.
 5. The system of claim 4, wherein electronic conglomerationsare replaced by at least one mechanical and at least one electroniccombination of conglomerations.
 6. The system of claim 4, or 5, whereinconglomerations are attachments to said systems of conglomerationsforming a plurality of dimensional and at least one layeredconglomerations.
 7. The system of claim 1, wherein phenomenon andphenomena metric code sequences are editable assembled and interruptiblesystem storage structures of at least one code line comprising: label,mean, variance, bound area, Gaussian Mixture Component descriptor set,and at least one appendable storage location for additional structuresspecific to means for identifying uniqueness of found phenomena andphenomenon described by a line of code in metric code sequence, and atleast one expandable blank location as a means for storage of system anduser decided appendages of other descriptors and functions and systemoutputs also pertinent to the uniqueness to particular phenomenon andphenomena described by said line of code and for means for codesequences describing to processer assemblers how processor gotphenomenon so as to repeat process externally and independent of need tore-take and reprocess raw data.
 8. The system of claim 7, wherein usereditable and appendable and communications abled storage locations, ofat least one unique line of metric code descriptors, of phenomenon andphenomena metric code sequence elements, sequenced as a line of code, ofcomputer readable data structures of each element of said code, iscomprised of at least one combination of at least one of: The storagelocation formatted for means for storage of at least one phenomenon andphenomena descriptor input of a matrix of at least size row one andcolumn one of at least one coordinate axis dimension, The storagelocation formatted for means for storage of at least one phenomenon andphenomena descriptor input of at least one numerical set structuredresult, The storage location formatted for means storage of at least onephenomenon and phenomena descriptor input of at least one character setstructured result, The storage location formatted for means for storageof at least one function output set, of set structure format assembledto that format of system's communications microcode instruction sequenceprocess, of at least one phenomenon and phenomena metric code sequenceelement, function analyzed, per said tracking system conglomerationprocess iteration, The storage location formatted for means for storageof at least one function output sequence set, of set structure formatassembled to that format of system's communications microcodeinstruction sequence process, of at least one phenomenon and phenomenametric code sequence, function analyzed, per at least one said trackingsystem conglomeration process iteration, The storage location formattedfor means for storage of at least one layer and at least one coordinatedimension of at least one sub-phenomenon phenomenon and phenomena metriccode structure set of at least one sub-phenomena metric code structureelement, The storage location formatted for means for storage of atleast one biological hardware capture and communications abled assembleroutput, translated by said tracking system as means to make data usable,data set structure, The storage location formatted for means for storageof at least one graphic, said tracking system conglomerationcommunications abled hardware attached, capture, and communicationsabled, assembler translated by said tracking system as a means to makedata usable, data set structure, The storage location formatted formeans for storage of a hard copy printed capture, processed by at leastone scanning of at least one framing and resending to systemconglomeration communications abled assembler translator by saidtracking system as a means to make data usable, of at least one capture,of at least one graphic captured, as a data set structure, The storagelocation formatted for means for storage of a system plotted frame andgraphic capture, processed by at least one scanning of at least oneframing of capture, and resending capture to system conglomerationcommunications abled assembler translator by said tracking system as ameans to make data usable, of at least one frame capture, of at leastone frame of graphic captured, as a data set structure, The storagelocation formatted for means for storage of a video sequence and videocaptured time-ranged frame of graphic of interest, processed by at leastone scanning of frame and resending to system conglomerationcommunications abled assembler translator by said tracking system as ameans to make data usable, of at least one frame capture, of at leastone graphic captured, as a data set structure, The storage locationformatted for means for storage of an audio sequence and audio capturedof at least one time and at least one frequency ranged, frame, plottedas a graphic of interest, processed by at least one scanning of time andfrequency, ranged, capture frame and resending to system conglomerationcommunications abled assembler translator by said tracking system as ameans to make data usable, of at least one frame capture, of at leastone graphic captured, as a data set structure, The storage locationformatted for means for storage of said conglomeration system outputdisplayed visually to a graphic, and assembler translated to at leastone graphic data set structure, The storage location formatted for meansfor storage of system conglomeration's component control operationalsequence sets, translated by operation assembler code builders, tooperation and sequence sets of interruption code step representations,The storage location formatted for means for storage of said trackingsystem conglomeration's component output control and input controlsequence sets, translated by operation assembler code builders, tooperation and sequence sets of input and output sequence process codestep representations, The storage location formatted for means forstorage of said tracking system conglomeration's component outputcontrol and input control encryption and decryption sequence sets,translated by operation assembler code builders, to operation andsequence sets of input and output sequence process code steprepresentations.
 9. The system of claim 8, wherein elements of code inphenomenon and phenomena metric code sequences are properly formattedand structured and initialized as pre-processing, null value, editableplace holders, by said tracking system conglomeration.
 10. The system ofclaim 8, wherein said tracking system conglomerations are communicatingas a means to finding, in static and dynamic designs of said trackingsystem conglomeration component layout structures, a final, raw dataprocessed then assembled and disconnected said tracking system generatedphenomenon metric code sequence, created upon completion of at least onecommunications process of at least one iteration of at least oneconglomeration of said tracking system component set comprisingcombinations of at least one of: at least one means for processing andstructuring editable and system and user interruptible phenomenon andphenomena metric code sequences, at least one phenomenon microcodeassembler means for at least one appendage to a storage container memorypoint of entry, of a metric code sequence structure, suitable for bothsystem process and text file portable storage transfer to components incommunications with system output ports, at least one phenomenon memorymodule as means for storage of raw data and at least one iteration andat least one layer of phenomenon and phenomena metric code sequencesstructured to be system processed and text file transported for meansfor phenomenon and phenomena data analysis on external and systemcommunication connected ports, at least one general componentcommunications memory module means for user and system interruptiblecomponent communications access to editable phenomenon and phenomenaassembled metric code sequences, at least one microcode handler andmicrocode creator means to process and re-process generated phenomenonand phenomena metric code updates and appendages through at least onecommunications level and layer of adaptive feedback communicationspaths, of arranged said tracking system components, arrange for at leastone iteration of corrective improvements to elements of phenomenon andphenomena metric code sequences, at least one means for communicationsbetween memory and processors and at least one time and power disableabled switchable data gather device, attached to system components andsystem attachments, all communications abled, both needing access andinterruption control, of at least one layer and at least one dimension,of said data gather devices, in communications with memory for storageand tracking processing of principle raw data, at least one means forsupplying power to all phenomena and phenomena detector andcommunications devices, and said system conglomerations, at least oneinput means for user to interrupt system, and components, incommunications with same tracking system, for editing and restarting atany point in process, at least one code sequence element, of phenomenonand phenomena metric code sequences, at least one output means for userto interact with the system data input means so that visuals of outputat interrupted points of processes, completed by at least one iterationof phenomenon and phenomena code sequence processing, of phenomenon andphenomena code sequences, have a point of user interaction and a pointin storage locations to output text file to file transportation controland its output port, at least one data sensor device means to activateat least one system conglomeration data gathering device, at timeintervals, and when thresholds of sensors are reached, at least onemeans to take system generated phenomenon and phenomena metric codeoutput external from system as a means to graphic conversiontransformation of system generated output, and process said graphictransformation output externally, and then re-introduce thetransformation at interruption point for processing, and performappendages to phenomenon and phenomena metric codes sequence structuresas a result of transformation, and to internally edit and appendphenomenon and phenomena in system at locations interrupted at user andsystem selected component locations, and system communicated with datagather device for means for re-processing phenomenon and phenomenondetection at interrupted phenomenon and phenomenon detection processingmodified output as part of a means for at least one iteration of anadaptive feedback system, At least one timing device for means forconglomeration component timing control of processing raw dataconglomerations from input gathering systems, and appendable phenomenonand phenomena metric sequence code assemblers and sequence codebuilders, and for output control and adaptive process timed sequencing,and for communications paths dynamic to timer control, and forcommunication switch control of input user control interrupters, Atleast one switching network means for taking timed raw data input, forprocessing system and user editable phenomenon and phenomena metric codesequences, and for system unit operations testing validations.
 11. Thesystem of claim 10, wherein the said tracking system communicationsabled data gather device is a biotechnology gathering device of outputand memory received input and output means, interruptible, and of propersystems communications ability and of data translation processors andassociated outputs, with means to assist as a system means to gather rawdata for input to a system conglomeration's adaptive processor, and itsassemblers, for means for processing and appending editable phenomenonand phenomena metric code sequences.
 12. The system of claim 10, whereinthe system communications abled data gather device is a scanninggathering device of hard output and screen display output and externaldisplay output of memory received input and output, interruptible, andof proper systems communications ability with data translationprocessors and associated outputs, to assist as a said tracking systemmeans to gather raw data for input to a system conglomeration's adaptiveprocessor and its assemblers for means for processing and appendingeditable phenomenon and phenomena metric code sequences.
 13. The systemof claim 10, wherein the system communicated data gather device is aportable data file communicated to the system conglomeration storagelocation as pre-formatted in a phenomenon and phenomena metric codesequence to be used and processed in its whole, part, and finality stateby said tracking system and attached components.
 14. The system of claim10, wherein input data is supplied to system conglomeration as segmentedmemory of phenomenon and phenomena metric code elements of usercontrolled and timed inputs.
 15. The system of claim 10, wherein inputdata is supplied to said tracking system conglomeration, as segmentedmemory of phenomenon and phenomena metric code sequence elements, inreal-time from system conglomeration attachment communications ports,wherein said system has no user interruption of phenomenon and phenomenaprocessing and no resulting metric code assembly and appendagesdetermined by user interruption of said tracking system, unless systeminterruption is pre-programmed as a means for further processingaccuracy of phenomenon and phenomena by feedback adaptive conglomerationcommunications active said tracking system components.
 16. The system ofclaim 10, wherein interruption of phenomenon and phenomena processingassemblers, for means for further raw data input gathering andphenomenon and phenomena metric code structure assembly, is dependent ondata gather devices activating sensors on said tracking system's datagathering devices.
 17. The system of claim 16, wherein there is at leastone sensor group, number in quantity of at least size one, that areexternal and in communications with system conglomeration input gatherdevices.
 18. The system of claim 10, wherein an additional component isa conglomeration communications abled encryption assembler, translatingat least one phenomenon and phenomena metric code element, of itsphenomenon and phenomena metric code structure, into at least oneencryption code sequence stored in an element storage location of samephenomenon and phenomena metric code structure, as a means formodulating further processed phenomenon and phenomena metric codesequences received as input by at least one said system conglomeration'sdata gather device and its transceiver controller device.
 19. The systemof claim 18, wherein an additional component is a conglomerationcommunications abled decryption assembler, translating at least onephenomenon and phenomena metric code element of encryption codesequence, of its structure, into at least one de-encryption codesequence, stored in an element storage location of same phenomenon andphenomena metric code structure, as a means for demodulating furtherprocessed phenomenon and phenomena metric code sequences received asinput by at least one said system conglomeration's data gather deviceand its transceiver controller device for means for reproducing to atleast approximate clarity the original phenomenon or phenomena metriccode sequence encrypted by said encryption device.
 20. The system ofclaim 1 or 18, wherein transceiver of said tracking system comprises atleast one means of: transmit and receive means for raw data gathered bysaid tracking conglomeration system and its attachments, transmit andreceive means for assembly and build of phenomenon and phenomena metriccode sequences from processed raw data gathered by same said trackingconglomeration system, and its attachments, and processing andassembling phenomenon and phenomena metric code sequence structures formeans for input to said tracking system conglomeration's storage andphenomenon and phenomena processing, transmit and receive video formatsfor means for assembly, format translation as means to make datareadable by said tracking system, and build of phenomenon and phenomenametric code sequences from processed raw data gathered by transceiver,and said tracking system conglomeration, as at least one scanned videoframe capture, transmit and receive audio formats for means forassembly, format translation as means to make data readable by saidtracking system, and build of phenomenon and phenomena metric codesequences from processed raw data gathered by transceiver, and trackingsystem conglomeration, as at least one scanned audio frame capture,transmit and receive video time-ranged formats for means for assembly,format translation as means to make data readable by said trackingsystem, and build of phenomenon and phenomena metric code sequences fromprocessed raw data gathered by transceiver, and tracking systemconglomeration, as at least one scanned video time-ranged frame capture,transmit and receive audio time-ranged formats for means for assembly,format translation as means to make data readable by said trackingsystem, and build of phenomenon and phenomena metric code sequences fromprocessed raw data gathered by transceiver, and tracking systemconglomeration, as at least one scanned audio time-ranged frame capture,transmit and receive video frequency-ranged formats for means forassembly, format translation as means to make data readable by saidtracking system, and build of phenomenon and phenomena metric codesequences from processed raw data gathered by transceiver, and trackingsystem conglomeration, as at least one scanned video frequency-rangedframe capture, transmit and receive audio frequency-ranged formats formeans for assembly, format translation as means to make data readable bysaid tracking system, and build of phenomenon and phenomena metric codesequences from processed raw data gathered by transceiver, and trackingsystem conglomeration, as at least one scanned audio frequency-rangedframe capture, transmit and receive signal in frequency-domainsegmented-ranged formats for means for assembly, format translation asmeans to make data readable by said tracking system, and build ofphenomenon and phenomena metric code sequences from processed raw datagathered by transceiver, and tracking system conglomeration, as at leastone scanned frequency-domain segmented-ranged frame capture as a meansfor translating from phenomenon and phenomena metric code sequencessignal characteristics captured in said scan, transmit and receivesignal in time-domain segmented-ranged formats for means for assembly,format translation as means to make data readable by said trackingsystem, and build of phenomenon and phenomena metric code sequences fromprocessed raw data gathered by transceiver, and tracking systemconglomeration, as at least one scanned time-domain segmented-rangedframe capture as a means for translating from phenomenon and phenomenametric code sequences signal characteristics captured in said scan,transmit and receive speech signals in frequency-domain FourierTransformed in display segmented-ranged formats for means for assembly,format translation as means to make data readable by said trackingsystem, and build of phenomenon and phenomena metric code sequences fromprocessed raw data gathered by transceiver, and tracking systemconglomeration, as at least one scanned segmented-ranged frame captureas a means for translating from phenomenon and phenomena metric codesequences signal characteristics captured in said scan, transmit andreceive voltage and current in display segmented-ranged formats, formeans for assembly, format translation as means to make data readable bysaid tracking system, and build of phenomenon and phenomena metric codesequences from processed raw data gathered by transceiver, and trackingsystem conglomeration, as at least one scanned segmented-ranged framecapture as a means for translating from phenomenon and phenomena metriccode sequences for voltage and current characteristics captured in saidscan, transmit and receive function plots and photos in displaysegmented-ranged formats, for means for assembly, format translation asmeans to make data readable by said tracking system, and build ofphenomenon and phenomena metric code sequences from processed raw datagathered by transceiver, and tracking system conglomeration, as at leastone scanned segmented-ranged frame capture as a means for translatingfrom phenomenon and phenomena metric code sequences for function andphoto characteristics captured in said scan.
 21. The system of claim 10,wherein speech is data captured by data gather devices and transceiversof said system tracking conglomeration, then speech signal processedinto spectral representations by audio assemblers and processors, thenscans and translations of displays of capture-frames spectralrepresentations are sent to phenomenon and phenomena metric codesequence assemblers as means for comparison to past and present portablephenomenon and phenomena metric code sequences for speaker identitymatching to past and present raw code converted also to phenomenon andphenomena metric code sequences for lookup table matched event andbackground noise eliminations and to determine and store phenomenon andphenomena metric code structure change errors.
 22. The system of claim10, wherein audio is data captured by data gather devices andtransceivers of said system tracking conglomeration, then signalprocessed into original signal and spectral representations by audioassemblers and processors, then scans and translations of displays ofcapture-frames of original and spectral signal representations are sentto phenomenon and phenomena metric code sequence assemblers as means forcomparison to past and present portable phenomenon and phenomena metriccode sequences for original signal characteristics identity matching topast and present raw code converted also to phenomenon and phenomenametric code sequences for lookup table matched event and backgroundnoise eliminations and to determine and store phenomenon and phenomenametric code structure change errors.
 23. The system of claim 10, whereinthermal data is captured by data gather devices and transceivers of saidsystem tracking conglomeration, then processed into temperature andimage and spectral representations by thermal data assemblers andprocessors, then scans and translations of displays of capture-frames oforiginal and thermal package representations are sent to phenomenon andphenomena metric code sequence assemblers as means for comparison topast and present portable phenomenon and phenomena metric code sequencesfor original image and temperature characteristics identity matching topast and present raw code converted also to phenomenon and phenomenametric code sequences for lookup table matched event and backgroundnoise eliminations and to determine and store phenomenon and phenomenametric code structure change errors.
 24. The system of claim 10, whereinvoltage, current, power and energy as physics data is captured by datagather devices and transceivers of said system tracking conglomeration,then processed into physics specific representations by data assemblersand processors, then scans and translations of displays ofcapture-frames of original and electrical physics packagedrepresentations are sent to phenomenon and phenomena metric codesequence assemblers as means for comparison to past and present portablephenomenon and phenomena metric code sequences for original physicspackage characteristics identity matching to past and present raw codeconverted also to phenomenon and phenomena metric code sequences forlookup table matched event and background noise eliminations and todetermine and store phenomenon and phenomena metric code structurechange errors, especially useful for a means for tracking changes inelectromagnetic fields and infrared radiation and antenna gain patterndetection.
 25. The system of claim 10, wherein sonar signal data iscaptured by data gather devices and transceivers of said system trackingconglomeration, then signal processed into original signal and spectralrepresentations by assemblers and processors, then scans andtranslations of displays of capture-frames of original and spectralsignal representations are sent to phenomenon and phenomena metric codesequence assemblers as means for comparison to past and present portablephenomenon and phenomena metric code sequences for original signalcharacteristics identity matching to past and present raw code convertedalso to phenomenon and phenomena metric code sequences for lookup tablematched event and background noise eliminations and to determine andstore phenomenon and phenomena metric code structure change errors. 26.The system of claim 10, wherein magnetic resonance imaging data iscaptured by data gather devices and transceivers of said system trackingconglomeration, then MRI data processed into original data and MRIsignal and image representations by MRI assemblers and processors, thenscans and translations of displays of capture-frames of original and MRIdata representations are sent to phenomenon and phenomena metric codesequence assemblers as means for comparison to past and present portablephenomenon and phenomena metric code sequences for original MRIcharacteristics identity matching to past and present raw code convertedalso to phenomenon and phenomena metric code sequences for lookup tablematched event and background noise eliminations and to determine andstore phenomenon and phenomena metric code structure change errors. 27.The system of claim 21, 22, 23, 24, 25, or 26, wherein data captures aretranslated into encryptions by extraction of elements of the phenomenonand phenomena metric code sequence and modulating onto another dataformat.
 28. The system of claim 1, wherein file compression formats arechanged as means for manipulating static and dynamic data frame capturecontents as means for providing new detections of phenomenon andphenomenon metric code sequences not otherwise detected in singlesampled file formats and therefore providing the said tracking systemadaptive feedback system error thresholds for adaptive feedbackprocessing within said tracking system conglomeration to increase anddecrease means for phenomenon focus needs and if necessary to enhanceencryption and decryption possibilities when encryption and decryptionis desired.
 29. The system of claim 1, wherein digital file formats arechanged as means for manipulating static and dynamic data frame capturecontents as means for providing new detections of phenomenon andphenomenon metric code sequences not otherwise detected in singlesampled file formats and therefore providing the said tracking systemadaptive feedback system error thresholds for adaptive feedbackprocessing within said tracking system conglomeration to increase anddecrease means for phenomenon focus needs and if necessary to enhanceencryption and decryption possibilities when encryption and decryptionis desired.
 30. The system of claim 1, 28, or 29, wherein static anddynamic photo data is captured by said phenomenon and phenomena metriccode sequence repeatability tracking system conglomeration data gatherdevices and transceivers, then imaged processed into original image andspectral representations by system assemblers and processors, then scansand translations of displays of capture-frames of original and imagerepresentations are sent to phenomenon and phenomena metric codesequence assemblers and as means for comparison to past and presentportable phenomenon and phenomena metric code sequences processed fororiginal image characteristics identity and repeatability matching topast and present raw code converted images also processed to phenomenonand phenomena metric code sequences as a means for lookup table matchingand background noise eliminations and to determine and metric storechange errors.